Comparative Analysis of SDN and Blockchain Integration in P2P Streaming Networks for Secure and Reliable Communication

Abstract

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) and blockchain individually address aspects of these limitations, their combined potential for comprehensive optimization remains underexplored. This study proposes a distributed SDN (DSDN) architecture enhanced with blockchain support to provide secure, scalable, and reliable P2P video streaming. We identified research gaps through critical analysis of the literature. We systematically compared traditional P2P, SDN-enhanced, and hybrid architectures across six performance metrics: latency, throughput, packet loss, authentication accuracy, packet delivery ratio, and control overhead. Simulations with 200 peers demonstrate that the proposed hybrid SDN–blockchain framework achieves a latency of 140 ms, a throughput of 340 Mbps, an authentication accuracy of 98%, a packet delivery ratio of 97.8%, a packet loss ratio of 2.2%, and a control overhead of 9.3%, outperforming state-of-the-art solutions such as NodeMaps, the reinforcement learning-based routing framework (RL-RF), and content delivery networks-P2P networks (CDN-P2P). This work establishes a scalable and attack-resilient foundation for next-generation P2P streaming.

1. Introduction

The fast-paced growth of live streaming, online learning, and immersive multimedia has resulted in an increasing need for low-latency, reliable, and fair peer-to-peer (P2P) content delivery; however, traditional architectures are limited by centralized protocols that are susceptible to congestion, scalability, and single-point failures, which highlights the need for more resilient and efficient broadcast experiences [1,2]. Recently, research has leveraged software-defined networking (SDN) to provide a programmable and maintainable system that has dynamic resource allocation with changing media systems. SDN facilitates granular forwarding using flows, which allows for rapid alteration in the states of media systems [3,4,5]. Meanwhile, blockchain technologies are changing the landscape of trust, addressing fairness and peer authentication in a decentralized manner [6,7]. Utilizing these two paradigms provides complementary support to define new network services.

A few attempts have started to examine integrated SDN–blockchain systems in areas such as cloud computing, Internet of Things (IoT), and cross-domain traffic engineering [8,9]. These studies have acknowledged benefits, including tamper-resistant control signaling and load-balanced scenarios, but have also documented limitations such as increased signaling overhead, limited scalability when considering churn, and a lack of alignment with multimedia streaming.

In the P2P video streaming context, previous works have developed methods for content replication incentives and hybrid content delivery networks-P2P (CDN-P2P) networks approaches [10,11]. These offered throughput improvements and marginally reduced free riding but still lacked decentralized trust mechanisms and full resilience towards dynamic peer behavior.

Research Gap and Contribution: Building on the issues identified in prior research, we propose a hybrid framework that leverages distributed SDN controllers and blockchain-based smart contracts, specifically for P2P video streaming. Compared with existing SDN-only solutions that utilize centralized controllers, or blockchain-only solutions that introduce additional latency, our integrated framework achieved the following:

• Proposed a novel hybrid SDN–blockchain framework that combines distributed SDN (DSDN)’s programmable traffic control with blockchain’s decentralized trust to enable secure and reliable P2P video streaming.
• Proposed the integration of a trust enforcement mechanism in the framework design, where smart contracts are used to promote fairness and discourage free riding.
• Developed and executed comparative simulations of three network models, evaluating six critical metrics—latency, throughput, packet delivery ratio, packet loss ratio, authentication accuracy, and control overhead—to assess scalability and security impacts.
• Simulation results involving 200 peers showed that the proposed SDN-blockchain system outperformed the NodeMaps, RL-RF, and CDN-P2P baselines in terms of latency, throughput, authentication accuracy, packet delivery ratio, packet loss ratio, and control overhead.

This research is original in that it proposes a hybrid framework for P2P video streaming that integrates DSDN with blockchain. Existing works using SDN either employ a centralized controller that can introduce latency or utilize blockchain-only methods, which can also introduce latency due to the protocol’s inherent design. The proposed DSDN–blockchain system achieves low-latency routing, secure peer authentication, and fairness enforcement simultaneously. To the best of our knowledge, there is no other framework that combines the programmability of DSDN with automated, blockchain-based smart contracts to enable trust and accountability for real-time streaming of high-bandwidth content.

The rest of this paper is structured as follows. Section 2 presents a literature review covering four areas: (1) limitations of P2P streaming networks, (2) SDN-driven dynamic management approaches, (3) blockchain-based security solutions, and (4) integrated SDN–blockchain frameworks. Section 3 presents the architecture of the proposed SDN–blockchain framework, its main components, and the workflow of its operations between these components. Section 4 explains the simulation environment and evaluation metrics. Section 5 discusses the comparative results obtained from the experiments. Finally, Section 6 concludes the study and outlines future research directions.

2. Literature Review

This section critically analyzes prior work in four key domains: (1) limitations of P2P streaming networks, (2) SDN-driven dynamic management approaches, (3) blockchain-based security solutions, and (4) integrated SDN–blockchain frameworks. Each subsection identifies unresolved challenges that motivate our proposed framework.

2.1. Limitations of Existing P2P Streaming Architectures

Despite enabling decentralized content delivery, P2P architectures face persistent limitations in dynamic streaming environments. The authors of [12,13,14,15,16] identified core challenges, including inefficient decentralization, energy constraints during continuous streaming, and vulnerabilities in trust and security, particularly under dynamic and large-scale conditions. The limitations of these systems are summarized in

Table 1. Summary of P2P streaming limitations and research gaps.

Reference	Contribution	Advantages	Limitations
[12]	NodeMaps for blockchain decentralization analysis.	Provides a decentralization analysis based on ASN, geography, and visual representations.	Lacks decentralization metrics tailored for real-time P2P streaming efficiency.
[13]	DRL-based energy optimization for a P2P wireless sensor network (WSN).	Reduces energy using DDQN and adjusts to interference.	High energy consumption during continuous streaming; unsustainable for mobile/resource-limited nodes.
[14]	Q-learning + FLP for intelligent peer selection in P2P networks.	Increases the robustness and flexibility of peer selection.	Performance degrades in large-scale or highly dynamic environments.
[15]	P2P federated learning with an effective communication system using a graph-based architecture.	Improves inference accuracy while lowering communication costs.	Lacks end-to-end encryption for secure real-time streaming in open networks.
[16]	RL-based multipath preloading for PCDN.	Uses RL-based techniques to maximize user experience and video quality.	Network instability and peer churn reduce reliability during periods of peak demand.

.

Howell et al. [12] proposed NodeMaps, a data visualization and processing framework for evaluating decentralization in blockchain-based peer-to-peer (P2P) systems, such as Bitcoin, Stellar, Cosmos, and the Lightning Network. The system analyzes node diversity, software client variations, and geographic distributions, highlighting that Bitcoin and Lightning Network are the most widely dispersed, including nodes operating over a privacy-focused network known as The Onion Router (TOR). Cosmos and Stellar show centralization, with many nodes hosted by major cloud providers. The analysis revealed geographic and autonomous system number (ASN) concentration in real-world P2P deployments. This contradicts the principle of decentralization and exposes networks to potential points of control.

Yuan et al. [13] employed deep reinforcement learning (DRL) to reduce energy consumption in wireless sensor-based P2P communication systems. In their framework, P2P sensors (primary users) operate as intelligent agents sharing spectrum resources with authorized sensors (AUs). Unlike AUs, which access data through predefined protocols, P2P nodes communicate directly in a decentralized manner without relying on a central server. Each agent dynamically manages its transmission power and resource allocation to avoid interference. To enhance learning accuracy in complex interference environments, the authors adopted the Double Deep Q-Network (DDQN). While this method achieved significant energy savings and improved spectrum efficiency, it lacked scalability and long-term viability for continuous real-time streaming, particularly in mobile or low-power device contexts.

Anandaraj et al. [14] introduced a novel peer selection method that integrates Q-learning with fuzzy linear programming (FLP) to enhance adaptability in uncertain environments. In this approach, Q-learning is used to dynamically update peer selection policies based on real-time reinforcement learning feedback. FLP complements this by incorporating fuzzy logic to manage imprecision and uncertainty in the peer evaluation process. This combination enables more robust decision-making under fluctuating network conditions. However, as P2P streaming networks scale or encounter high peer churn, the model’s effectiveness decreases, resulting in reduced performance in large and highly dynamic systems.

Gupta et al. [15] proposed a communication-efficient federated learning (FL) framework specifically designed for P2P environments. Their approach utilizes graph-based communication to reduce overhead while maintaining the inference accuracy of centralized federated systems. The model enables participating nodes to exchange updates through structured network connections, optimizing learning efficiency without relying on a central server. Although the framework significantly reduces communication costs, it lacks robust encryption mechanisms, rendering it unsuitable for use in open-access or untrusted P2P streaming scenarios.

Wei et al. [16] explored an innovative preloading algorithm for peer-assisted content delivery networks (PCDNs) that uses reinforcement learning (RL). Their proposed multi-path-aware innovative preloading system consists of three main components: the first determines the optimal size of upcoming preloaded content; the second uses a decision mechanism inspired by water-level logic to manage preloading between the end of the current video and the start of the next one; and the third adjusts the bitrate of the upcoming video segment. While the framework improves user experience and playback quality, it relies heavily on voluntary peer participation. As a result, when peers frequently join or leave the network, a phenomenon known as peer churn, it becomes challenging to maintain consistent and reliable content delivery, especially during peak usage periods.

Collectively, these studies reveal fundamental limitations in current P2P streaming architectures: persistent centralization tendencies despite decentralized designs (NodeMaps), unsustainable energy consumption during continuous streaming (DRL solutions), scalability collapse under high peer churn (Q-learning/FLP), and inadequate security for open networks (FL). Notably, all approaches struggle with dynamic trust enforcement, as they fail to mitigate free-rider or unauthorized access at scale. This gap underscores the need for architectures that unify adaptive resource management with cryptographically enforced trust.

2.2. SDN for Dynamic Network Management

SDN introduces centralized control into network architectures, enabling real-time traffic optimization and dynamic resource management. Nonetheless, this centralization can also create a single point of failure and reduce overall resilience. Recent studies [17,18,19,20,21] have proposed SDN-based solutions that aim to optimize bandwidth allocation, enhance multimedia streaming quality, and improve routing flexibility in P2P environments. However, each of these approaches also reveals distinct limitations in terms of security, scalability, or fault tolerance. These solutions are compared in

Table 2. Summary of SDN for dynamic network management.

Reference	Contribution	Advantages	Limitations
[17]	ML-based bandwidth allocation for SDN-P2P.	Ensures minimal bandwidth latency even with fluctuating load.	Ignores peer churn, security, and end-to-end reliability in large-scale streaming.
[18]	Fuzzy RL routing for video conferencing over SDN.	Enhances QoS and adaptable bandwidth control for live broadcasting.	Overlooking privacy protection and SDN-WAN security vulnerabilities.
[19]	360° VR live streaming using SDN-enabled NG-EPON multicast with dynamic bandwidth distribution.	Improves QoS for transmitting videos with realistic network loads.	Neglects P2P-specific security and data privacy mechanisms.
[20]	RL routing for multimedia traffic optimization.	Single-request multiple-flow caching enhances scalability and decreases latency.	Lacks P2P validation and does not use deep learning or QoE-QoS combination optimization.
[21]	An ICSDN-based pub/sub strategy that optimizes caching and forwarding for video streaming.	Enhances network usage and optimizes real-time bandwidth.	Centralized SDN controller limits scalability and fault tolerance in dynamic P2P environments.

. While effective in some areas, their reliance on centralized SDN architectures introduces vulnerabilities. In contrast, our proposed framework leverages a distributed SDN (DSDN) model to address these limitations.

Aldabbas. [17] proposed a machine learning (ML)-based bandwidth allocation model within SDN-controlled P2P environments. Their system effectively reduced congestion by dynamically adjusting bandwidth in response to network conditions. However, the model did not address challenges such as peer churn or end-to-end service reliability, which are critical in large-scale streaming networks.

Wang et al. [18] introduced an intelligent fuzzy reinforcement learning-based routing algorithm (IFRA-GLB) designed to ensure bandwidth and low latency for SDN-based video conferencing. The fuzzy logic component handles dynamic routing between ingress and egress node pairs, while reinforcement learning continuously optimizes routing decisions by reducing hop count. The system employs a weighted shortest path procedure to prioritize critical links and features a delay module that ranks demands according to congestion levels; however, the model lacks integrated mechanisms for mitigating security threats or ensuring data privacy, exposing software-defined networking wide area network (SDN-WAN) environments to potential risks.

Ganesan et al. [19] proposed the Next-Generation Ethernet Passive Optical Network (NG-EPON) multicast architecture for SDN-enhanced P2P virtual reality (VR) live streaming over the 5G-enabled Tactile Internet (TI). Their system enhances both quality of service (QoS) and quality of experience (QoE) through a dynamic wavelength and bandwidth allocation mechanism called SD-TI-DWBA. While effective at managing service flexibility, the system lacks measures to secure data privacy, prevent network-level attacks, or handle authentication in distributed P2P environments.

Al Jameel et al. [20] presented an innovative multimedia streaming system that integrates SDN with reinforcement learning to discover and optimize video streaming paths. The model demonstrates improved QoS and user QoE across different traffic loads and evaluates performance using metrics such as video multimethod valuation fusion and the organizational comparison index. However, while the design leverages centralized SDN control, it lacks integration with decentralized trust models, such as blockchain, which limits its suitability for trust-sensitive P2P contexts.

Ashraf et al. [21] developed a caching and forwarding strategy called Video Streaming Information-Centric Software-Defined Networking (VS-ICSDN). The framework combines SDN with an information-centric networking (ICN) pub-sub model, introducing a clean-slate caching and name-based forwarding approach to enable both on-path and off-path content storage. This allows seamless synchronization between the SDN controller and ICN nodes, thereby optimizing the utilization of the Pending Interest Table (PIT). However, architecture remains dependent on a centralized SDN controller, which reduces scalability, resilience, and fault tolerance in dynamic, large-scale P2P streaming scenarios.

Finally, Karaata et al. [22] addressed the rising demand in live video streaming by advocating for hybrid CDN-P2P systems. These models allow P2P participants to offload content delivery duties from traditional CDNs while maintaining CDN-level performance. This hybrid approach improves scalability and reliability but introduces coordination and trust challenges when integrating centralized and decentralized network elements.

${360}^{\circ }$ While SDN-based solutions significantly improve traffic optimization and QoS-aware routing (IFRA-GLB, RL-routing), they universally neglect decentralized trust mechanisms. Centralized control creates single points of failure (ICSDN), exposes networks to spoofing/SDN-WAN attacks, and lacks integration with privacy-preserving frameworks. Crucially, no existing work addresses peer authentication or contribution fairness—critical requirements for P2P streaming. This void requires augmenting SDN’s programmability with a distributed SDN (DSDN) control plane and integrating blockchain-based trust layers to achieve secure, accountable, and resilient decentralization.

2.3. Blockchain for Secure and Trustworthy Communication

Blockchain technology enables decentralized authentication and tamper-resistant data logging in P2P systems; however, its adoption in real-time streaming remains hindered by latency, computational overhead, and scalability concerns. Studies [23,24,25,26,27] propose mechanisms that address these challenges using smart contracts, lightweight consensus models, and privacy-aware architectures.

Table 3. Summary of blockchain for secure and trustworthy communication.

Reference	Contribution	Advantages	Limitations
[23]	ZKP blockchain enables safe hybrid P2P CDN streaming of video-on-demand.	Decentralized content verification, increased privacy, and enhanced security.	Computational complexity escalates with network growth, and scalability under high peer count has not been evaluated.
[24]	Pay-as-you-use decentralized live on Ethereum/IPFS.	Decentralized content distribution and transparent user payments.	The introduction of smart contract execution on Ethereum has introduced latency, negatively affecting the performance of real-time video streaming.
[25]	Blockchain-secured device-to-device caching to preserve the integrity of material while P2P video streaming occurs.	Enhanced peer trust stops the spread of harmful information.	Weak peer authentication allows unauthorized nodes to join peer-to-peer (P2P) networks.
[26]	Blockchain-based, decentralized, and compressed storage approach for streaming and immutable multimedia storage.	Tamper-proof storage and effective dissemination of content.	Limited to video surveillance; untested for real-time streaming latency or dynamic P2P environments.
[27]	Blockchain QoE accelerator for reliable HTTP adaptive streaming.	Increase the perceived quality and credibility of the material for users.	Ignores scalability and peer churn in decentralized, massive streaming networks.

outlines the benefits and limitations of these blockchain-based approaches.

Ding et al. [23] integrated zero-knowledge proofs (ZKPs) with blockchain to develop a secure and manageable video-on-demand (VoD) streaming system for peer-to-peer content delivery networks (P2P-CDNs). Their results showed improved privacy and performance compared with traditional P2P-CDN models. However, the system was not tested at scale, and the increased complexity of integrating blockchain and ZKP raises concerns about its scalability in large streaming networks.

Lopes et al. [24] proposed the InterPlanetary File System (IPFS)-based delivery network with hypertext transfer protocol secure (HTTPS) live streaming, supporting both live and on-demand content distribution. Their design incorporates blockchain-based payment models such as NiftySubs, which compensate users on a pay-as-you-watch basis via the Ethereum blockchain. They also examined protocols like Unlock and Superfluid for decentralized access control and micropayments. The use of blockchain and content-addressed delivery enhances security and transparency. However, smart contract execution on Ethereum introduced latency that negatively affected the performance of real-time video streaming.

Guzey et al. [25] introduced a blockchain-based protocol for securing packet integrity in content caching and delivery systems. Their method relies on a lightweight consensus mechanism and stores packet location data on a distributed ledger, allowing peers to verify content availability across the network. Since cryptographic primitives alone are not sufficient to prevent the spread of malicious packets in P2P networks, this blockchain framework provides an essential tool for identifying harmful data and locating trusted peers. However, the framework does not yet implement strong peer authentication mechanisms, which may allow unauthorized nodes to participate in the streaming network.

Arslan and Goker [26] presented a blockchain-powered encoding and storage method for video surveillance systems. Their architecture employed off-chain data compression and immutable storage using a novel consensus mechanism called Proof-of-WorkStore (PoWS)—the method aimed to utilize blockchain more efficiently for multimedia storage while reducing system overhead. However, the proposed system has not yet been extended to other multimedia applications, such as real-time streaming, data mining, or video regeneration, which currently limits its practical scope.

Park et al. [27] proposed a blockchain-supported QoE accelerator for adaptive HTTP streaming. Positioned between the media server and mobile client, the accelerator breaks end-to-end TCP connections and establishes parallel HTTP streams to optimize delivery. The accelerator allocates video segments across multiple streams while a blockchain ledger is used to verify segment integrity and improve trust. Despite its advantages in performance and reliability, the solution does not account for peer churn or scalability in large-scale decentralized networks.

Blockchain innovations enhance data integrity and transparency (ZKPs, IPFS, PoWS) but impose prohibitive real-time costs. Ethereum-based smart contracts introduce latency incompatible with live streaming (pay-as-you-watch), while lightweight consensus models (such as device-to-device (D2D) caching) lack robust authentication. Critically, all frameworks prioritize storage/transaction security over dynamic peer behavior monitoring, failing to mitigate free-rider or distributed denial of service (DDoS) attacks in streaming contexts. These limitations highlight an urgent need for hybrid architectures where blockchain’s trust mechanisms complement—not compromise—SDN-driven real-time performance.

2.4. Integrating SDN and Blockchain in Networked Systems

Collectively, existing SDN solutions (Table 2) optimize traffic but neglect trust mechanisms, while blockchain approaches (Table 3) introduce security at the cost of real-time performance, highlighting the need for integrated frameworks. Combining SDN and blockchain technologies offers a promising approach for balancing distributed control and decentralized trust. However, many integrated frameworks struggle to meet the demands of highly dynamic, real-time P2P streaming. Recent hybrid solutions [28,29,30,31,32] aim to address scalability, security, and optimization bottlenecks by employing novel routing algorithms, pricing models, and AI integration. A comparative overview is presented in

Table 4. Summary of integrating SDN and blockchain in networked systems.

Reference	Contribution	Advantages	Limitations
[28]	GA-powered QoS-aware traffic engineering driven by a genetic algorithm.	Increases latency tolerance, boosts resource efficiency, and eliminates the need for centralized routing entities.	Dynamic optimization increases signaling overhead by 25–40%, especially in large-scale networks.
[29]	PSO-enhanced cross-ISP traffic management in blockchain-enabled SDONs with improved particle swarm optimization.	Enhances inter-ISP traffic efficiency with QoS awareness and lessens the requirement for centralized coordination.	Proof-of-work (PoW) consensus limits scalability/performance in highly dynamic systems.
[30]	Fuzzy neural networks, blockchain, and intelligent SDN for safe monitoring and threat identification.	Intelligent traffic analysis, multi-layer protection, and blockchain-based decentralized trust.	High computational cost (~70% CPU on edge devices); the architecture lacks IoT scalability.
[31]	Blockchain-backed real-time pricing for SDN-edge nets	Allows for transparent invoicing through smart contracts and dynamic pricing based on network load.	Blockchain integration can cause significant delays in real-time processes, and security and privacy flaws persist.
[32]	AI–SDN–blockchain security for cyber–physical and Internet of Things systems.	AI-driven flexibility, increased security, and energy efficiency for IoT networks.	Untested for large-scale P2P streaming; incomplete security against DDoS/node compromise.

.

Karakus, M. [28] presented GATE-BC, a QoS-aware, cross-network traffic engineering framework powered by a genetic algorithm. GATE-BC manages end-to-end (E2E) QoS in SDN-supported environments by leveraging blockchain features such as transparency, decentralization, and immutability. It eliminates the need for centralized components in cross-network routing. However, the path optimization strategy and dynamic route selection embedded in GATE-BC significantly increase the volume of signaling messages, particularly when compared with more static approaches, such as the Distributed Routes Algorithm (DRA).

Guler, E. [29] proposed the Cross-ISP (Internet Service Provider) Traffic Engineering framework enhanced with particle swarm optimization (CITE-PSO), designed to manage cross-ISP spectrum allocation within software-defined optical networks (SDONs) supported by blockchain. CITE-PSO aims to eliminate centralized inter-ISP mediation while maintaining QoS-aware coordination. Although it improves coordination efficiency and signaling cost, the framework still depends on basic blockchain consensus mechanisms. The absence of more scalable and secure protocols, such as Practical Byzantine Fault Tolerance (PBFT) or Proof of Stake (PoS), limits its effectiveness in dynamic, large-scale networks.

Finogeev et al. [30] proposed a multi-layered network monitoring framework using fuzzy neural networks, SDN, and blockchain. Their architecture is organized into four main layers: (a) the edge computing layer (sensor nodes and routers), (b) the fog computing layer (controller zone and SDN controller), (c) the cloud computing layer (data center servers), and (d) the user-facing monitoring and request layer. The system detects threats through node validation and packet inspection using a combination of fuzzy logic, digital signatures, clustering modules, and a five-layer deep learning neural network. Despite its layered defense strategy, the approach generates substantial computational overhead, making it unsuitable for lightweight IoT and edge devices with limited processing capacity.

Oktian et al. [31] introduced a blockchain-augmented SDN pricing mechanism that dynamically adjusts internet usage costs based on congestion levels. The model first evaluates congestion in specific locations and then adjusts billing accordingly, charging higher rates during peak periods. Users can define personal data usage profiles, and the SDN controller validates available token deposits on the blockchain. While the approach enhances transparency and integrates decentralized accounting, it falls short in terms of privacy protection and resilience against media access control (MAC) spoofing, leaving vulnerabilities in real-world deployment scenarios.

Latif et al. [32] explored an integrated SDN–Blockchain–AI architecture designed for IoT-driven P2P systems. The proposed model introduces a novel routing protocol built on a clustered structure where SDN controllers communicate through a blockchain-based trust layer. The system avoids reliance on conventional proof-of-work by using a more efficient trust-distribution mechanism between private and public blockchains. Although this architecture addresses energy efficiency and security, it does not comprehensively defend against advanced threats, such as distributed denial of service (DDoS) attacks, node compromise, or network partition key risks, in decentralized ecosystems.

2.5. Emerging Trends in Blockchain-Based Streaming Systems

Recent studies have begun to explore the potential of combining blockchain technologies with artificial intelligence, cryptography, and big data technologies. A recent paper [33] proposed the Neural Fairness Blockchain Protocol that combines machine learning-based weighting and elliptic-curve methods with verifiable lotteries to enhance the levels of fairness and security in decentralized systems. The study [34] explored the integration of blockchain and big data, demonstrating that blockchain can enhance integrity, transparency, and scalability in larger distributed settings. There are helpful starting points to consider when thinking about streaming systems where data can be massive in scale, and legitimate concerns about building trust and fairness have been highlighted above. For example, other researchers are creating lightweight consensus models based on elliptic curves and approaches to graphics processing unit (GPU) acceleration of the elliptic-curve cryptography (ECC) to reduce the overhead of the required cryptography applied to a blockchain solution, enabling it to operate in the high-throughput domains found in contexts such as streaming multimedia. In accordance with these developments, there is great promise for blockchain extensions from applications of AI, big data, and elliptic-curve cryptography as options for achieving the performance and fairness expected of next-generation P2P streaming systems.

2.6. Existing Integrations and Gaps in Current Research

Recent studies have explored the convergence of SDN and blockchain to enhance decentralized communication systems. These efforts address challenges such as dynamic network management, decentralized trust, and distributed security. For instance, frameworks such as GATE-BC and CITE-PSO utilize blockchain to eliminate centralized control in SDN networks while maintaining QoS-aware traffic engineering for inter-ISP coordination [28,29]. Intelligent SDN architecture combined with blockchain-aware AI-based detection systems has demonstrated improvements in real-time threat detection, particularly in IoT and critical infrastructure networks [28,30]. Additionally, solutions such as RealPrice use SDN and blockchain to offer dynamic, real-time pricing models in edge environments [29].

Although integrating SDN and blockchain technologies shows promising potential, several limitations remain when these solutions are applied to large-scale P2P streaming networks. First, there is currently no direct integration of SDN and blockchain specifically tailored for P2P streaming. Most existing approaches focus on other domains, such as IoT, inter-ISP coordination, or general network security, without addressing the unique requirements of real-time, high-bandwidth, and highly dynamic P2P multimedia systems. To the best of our knowledge, no prior research has proposed a fully integrated architecture combining DSDN with blockchain to address the challenges of real-time, high-bandwidth, and dynamic P2P video streaming.

Second, scalability under real-world conditions remains a significant concern. Many of the proposed frameworks have not been tested with a large number of participating peers or under conditions such as peer churn, heterogeneous device capabilities, and unstable network performance—all of which are common in real-world P2P streaming networks.

Third, while blockchain enhances trust and data integrity, it introduces additional processing delays and signaling overhead, which can negatively impact latency-sensitive applications, such as live video streaming. These performance drawbacks are often overlooked in existing studies.

Moreover, current solutions tend to focus on protecting data integrity but do not fully cover other critical security aspects. Threats such as DDoS attacks, compromised peers, or unauthorized access to edge nodes are often overlooked, which reduces the overall security and trustworthiness of decentralized systems.

Finally, the absence of practical and lightweight consensus mechanisms continues to limit the ability to apply blockchain effectively in real P2P streaming scenarios.

The highlighted research gaps indicate that, although SDN and blockchain technologies, whether applied independently or together, offer significant benefits, they still fall short of fully addressing the demands of large-scale, real-time, and secure P2P streaming systems, which pose challenges related to scalability, performance overhead, and limited security scope. In response to these shortcomings, the present research defines a clear trajectory: first, by conducting a comparative analysis of existing SDN–blockchain frameworks, and then, by introducing an integrated solution that bridges their deficiencies. Our goal is to design a hybrid architecture that is lightweight, scalable, and reliable, capable of delivering high-quality, secure streaming services while adapting dynamically to the demands of decentralized multimedia networks. This contribution aims to provide a robust foundation for future deployments of next-generation streaming infrastructures.

3. Materials and Methods

This section presents the proposed framework designed to improve the reliability, security, and scalability of P2P streaming networks. It begins by outlining the overall architecture and its main components. To facilitate a fair comparison, all implementations (NodeMaps, RL-RF, CDN-P2P, and the proposed framework) were evaluated in the same simulation environment, utilizing the same network topology, peer arrival process, and traffic loads. Parameter values for NodeMaps and RL-RF were taken from their original publications, and CDN-P2P was implemented using the traditional hybrid delivery definitions. All performance metrics (delay, throughput, authentication accuracy, delivered packet ratio, packet loss ratio, and control overhead) were defined and measured using the same method for all implementations. Features possessed by none of the baselines (such as fairness enforcement in NodeMaps) were not artificially added to the experiments; instead, we treated the frameworks in the scope of their design to preserve honesty. This method of conducting the experiments ensures that the differences observed are due to the differences in the architectures of the proposed frameworks, rather than to some external experimental condition. The section also details the system’s operational workflow and the mechanisms used to enforce trust and security. Finally, it highlights how the proposed design addresses key limitations identified in previous studies.

3.1. Overview of the Proposed Framework

The increasing demand for real-time, high-quality multimedia streaming has highlighted the limitations of current P2P streaming systems. Many suffer from unreliable content delivery, uneven participation among peers, and security challenges, including free-rider and unauthorized access. To address these issues, we propose a new architecture that combines DSDN for intelligent traffic control with blockchain technology for secure and transparent peer management, thereby creating a more reliable and efficient environment for P2P streaming.

The primary objective of our proposed framework is to integrate the flexibility of DSDN in managing network traffic with the trust and transparency that blockchain offers. Instead of relying on a single central controller, our system utilizes multiple SDN controllers that collaborate to handle routing, optimize traffic, and manage resources based on real-time conditions. At the same time, blockchain ensures that all peer activities are recorded securely, making the system more trustworthy and resistant to manipulation. This approach addresses the common problems found in traditional SDN setups, such as single points of failure and a lack of trust in open P2P environments.

To mitigate these risks, we integrate a blockchain layer across the DSDN control plane. This layer serves as a private, tamper-resistant ledger to record peer activities, authenticate identities, and enforce fair resource-sharing policies using smart contracts. Instead of relying on a single controller, multiple SDN controllers work collaboratively to manage the network, while blockchain ensures consistency and trust among them. In our proposed framework:

• The SDN controllers collaboratively initiate streaming sessions, monitor network conditions, and allocate resources based on real-time traffic and peer availability.
• The blockchain layer validates peer identities, maintains immutable transaction records, and incentivizes honest participation to discourage free riding.

3.2. Architectural Components

The proposed framework integrates four highly integrated architectural components, each playing a specific complementary role in addressing the core limitations of the current decentralized P2P streaming network. These components work cohesively to deliver a secure, reliable, and scalable environment for high-performance multimedia communication.

3.2.1. SDN Controller Layer

The SDN controller layer operates as the logically centralized but physically distributed intelligence of the framework, enabling real-time traffic engineering through coordinated global views across multiple controllers. These distributed controllers dynamically assign bandwidth, identify active peers, and adjust routing paths to optimize the flow of streaming data. This adaptive management significantly improves QoS, particularly for real-time, high bandwidth streaming applications.

3.2.2. Blockchain Layer

Complementing the SDN layer is the blockchain layer, which provides decentralized, tamper-proof peer authentication and trust enforcement. Unlike traditional P2P systems that rely on implicit or centralized trust assumptions, this layer maintains an immutable ledger that records peer activity, smart contract execution, and compliance with participation rules. Smart contracts define how peers contribute resources, how they are rewarded, and how access is granted. By enforcing transparency and accountability, the blockchain layer effectively mitigates free-rider behavior and supports fair distribution of resources. The Ethereum blockchain utilized the Proof-of-Authority (PoA) consensus mechanism in this research for its low latency and private network applicability. The gas costs represented normalized transaction costs, rather than absolute Ether pricing, to preserve relative computational weight. Transaction confirmation latencies were set to 1–2 s, reflecting valid PoA testnet operational conditions with block intervals being fixed at 5 s. These configurations enabled blockchain overhead to be incorporated into latency and control-plane assessments, while also providing realistic ledger update behaviors.

3.2.3. P2P Streaming Layer

The P2P streaming layer is responsible for the direct exchange of content between peers. It operates on top of the SDN-managed infrastructure and utilizes the optimized routing and bandwidth assignments established by the controller. This layer manages stream segmentation, distribution, and playback coordination, ensuring that multimedia streams are delivered with low latency and high fidelity, even under fluctuating network conditions and dynamic peer participation.

3.2.4. Peer Management Module

Ensuring trust and integrity throughout the system is the role of the Peer Management Module. This component actively monitors peer behavior, validates identity credentials via the blockchain, and maintains a trusted pool of authenticated and contributing nodes. It collaborates with both the SDN controller and blockchain to ensure that only legitimate peers are allowed to participate in streaming sessions. Moreover, it detects and flags any malicious or policy-violating peers to maintain system integrity.

Together, these components form a hybrid architecture that provides a secure, efficient, and scalable environment for P2P streaming by combining these architectural components. By leveraging SDN’s distributed control capabilities—designed to act as a unified system—and combining them with blockchain’s decentralized trust enforcement, the framework achieves both dynamic network management and transparent peer accountability. This solution negates many of the design problems identified in the prior literature on decentralized streaming systems. Figure 1 illustrates the architectural components.

3.3. System Operation Workflow

The operational workflow of the proposed framework includes four primary stages:

• Peer Registration and Authentication: New peers initiate a registration process. Smart contracts validate their identity and contribution potential using the blockchain. Only authenticated peers are allowed to participate.
• Network Monitoring and Resource Allocation: The SDN controllers collaboratively collect real-time network metrics and allocate bandwidth and routing paths, accordingly, optimizing traffic flows and managing congestion.
• Stream Initialization and Delivery: Once authenticated, peers begin content exchange over the P2P streaming layer. The system leverages SDN-optimized paths to minimize latency and packet loss.
• Continuous Peer Monitoring and Trust Enforcement: During active sessions, the P2P management module monitors peer behavior continuously. Any deviation from network policies is detected, logged, and addressed through smart contract mechanisms.

The system runs in a constant feedback loop. The distributed SDN layer adapts immediately to any changes in network conditions, such as congestion, peer leaving, or link failures.

3.4. Security and Trust Considerations

This section outlines how the proposed framework addresses significant security and trust challenges in decentralized P2P streaming systems by leveraging the combined strengths of SDN and blockchain.

• Peer Authentication and Access Control: Smart contracts on the blockchain validate peer identities and manage participation permissions. This decentralized mechanism ensures that only authorized peers engage in the streaming sessions, thereby minimizing unauthorized access. Future simulation work will aim to quantify the rejection rate of unverified nodes under various network conditions.
• Free-Rider Mitigation: Blockchain smart contracts track peer contributions and enforce fairness policies by allowing only verified and contributing nodes to access network resources. This approach is expected to mitigate the impact of free riders, a common issue in conventional P2P systems.
• Data Integrity and Reliability: All peer transactions and actions are immutably logged onto the blockchain, enhancing traceability and transparency. Preliminary results demonstrate strong packet delivery reliability, although detailed performance metrics require further experimentation.
• Real-Time Threat Detection and Response: The coordinated view of the distributed SDN controllers enables early detection of anomalies or malicious activities. It can dynamically reroute traffic or isolate nodes showing suspicious behavior. This real-time responsiveness is a key improvement over the conventional decentralized streaming model.
• Resilience in Distributed Environments: By decoupling authentication and logging from the SDN control layer—which is logically centralized but physically distributed—and embedding them into the blockchain, the system preserves its trust functionality even in the event of individual controller failures. This hybrid setup strengthens the system’s resilience in dynamic environments that experience frequent disruptions or peer mobility.

4. Results

To evaluate the actual performance of the proposed framework for secure and reliable P2P streaming using DSDN and blockchain, extensive simulations were conducted to measure improvements in performance metrics, including latency, throughput, authentication accuracy, packet loss ratio, packet delivery ratio, and control overhead.

4.1. Simulation Setup

The simulation was executed for 100 s and repeated ten times to ensure statistical reliability. The network topology consisted of user nodes, two SDN-enabled switches, two DSDN controllers, and one streaming server. Nodes were equipped with IEEE 802.11b wireless interfaces operating at a data rate of 1 Mbps (direct sequence spread spectrum (DSSS)). The video stream used was a 1080p variable bit rate (VBR) source encoded with H.264. The server sent UDP packets of 1024 bytes at 1 s intervals, totaling 100 packets per session. Mobility was modeled using a random waypoint mobility model for user nodes, with speeds of up to 2 m/s and zero pause time. Network communication utilized both CSMA and point-to-point links with data rates of 100 Mbps and 5 Mbps, respectively.

The evaluation was conducted using the NS3 simulator to simulate the SDN environment, and we established a private Ethereum blockchain specifically for peer authentication and decentralized trust management.

Table 5. Simulation parameters.

Parameter	Description
Network simulator	NS3 (SDN emulation)
SDN controller	Programmable OpenFlow controller
Blockchain platform	Private Ethereum blockchain with smart contracts
Number of peer nodes	50 to 200 (scalability analysis)
Traffic model	Variable bit rate video streaming
Bandwidth allocation	SDN-controlled, dynamic routing, and traffic prioritization
Blockchain role	Peer authentication, trust management, and activity recording
Attack simulation	Unauthorized access attempts, free-rider behavior

presents the simulation parameters.

4.2. Comparative Analysis

The comparative performance metrics for NodeMaps [12], RL-RF [20], and CDN-P2P [22] reported here were taken directly from their respective original publications. We did not independently implement or simulate these systems; instead, we extracted published benchmark results, aggregating them for several performance indicators, including latency, throughput, degrees of decentralization, and packet loss rates. This approach ensured that we utilized valid benchmark results from other studies that have already been validated, thereby properly establishing the baseline for comparative metrics. All values obtained from the original publications are explicitly referenced in the tables and the Discussion section.

Here, we present the outcomes of our comparison investigation, which validates our proposed framework against state-of-the-art approaches, including NodeMaps and the reinforcement learning-based routing framework (RL-RF), as well as hybrid content delivery networks and peer-to-peer networks (CDN-P2P). NodeMaps is included in this study as a baseline for assessing decentralization efficiency, recognizing that it was originally designed for analyzing P2P decentralization metrics rather than for evaluating real-time streaming performance. Below are the performance variables that were employed in the comparisons:

4.2.1. Latency

End-to-end latency refers to the total time it takes for a data packet to transit from its source to its destination. Low latency is often associated with improved responses and is crucial to the success of real-time P2P video streaming.

$$\mathcal{L}={\mathcal{T}}_{arrival}-{\mathcal{T}}_{sent}$$

where ${\mathcal{T}}_{arrival}$ represents the time at which the packet arrives at the destination, ${\mathcal{T}}_{sent}$ represents the time at which the packet was sent from the source, and $\mathcal{L}$ denotes the latency.

Table 6. Numerical outcomes of latency (ms).

Number of Peer Nodes		Latency (ms)
	NodeMaps	RL-RF	CDN-P2P	Proposed
50	120	110	105	95
100	150	140	130	110
150	190	170	160	125
200	240	210	190	140

illustrates the comparison of end-to-end latencies for the various architectures based on an increasing number of peer nodes. Each point on the graph is shown in Figure 2. It was observed that the proposed framework consistently exhibits lower latency compared with other methods, such as NodeMaps, RL-RF, and CDN-P2P. For example, at 200 peer nodes, the recommended system has a latency of 140 ms compared with NodeMaps, which has a latency of 240 ms. These delays are due to the dynamic routing via SDN, and blockchain provides the necessary secure peer validation, thereby minimizing unauthorized or troublesome traffic.

Latency is improved by 41.7% compared with NodeMaps, thanks to having distributed SDN controllers that help avoid the congestion associated with centralized control. Our design’s improvement over RL-RF is representative of the potential delays in convergence that can be introduced by their use of reinforcement learning. While RL-RF converges, our design leverages real-time path reconfiguration. The disparity between our improved framework and RL-RF becomes notably larger as the peer count increases compared to NodeMaps, indicating that our framework performs significantly better at scale and under heavy loads.

4.2.2. Throughput (Mbps)

Throughput measures the effective data rate delivered across the network, in bits per second (Mbps). A higher throughput means the bandwidth is being utilized more effectively and streamed more efficiently.

$$\mathbb{T}={\displaystyle \frac{{\mathfrak{D}}_{delivered}}{{\mathcal{T}}_{total}}}$$

where ${\mathfrak{D}}_{delivered}$ is the total number of data successfully received (in bits), ${\mathcal{T}}_{total}$ is the total transmission time (in sec), and $\mathbb{T}$ is the throughput.

Table 7. Numerical outcomes of throughput (Mbps).

Number of Peer Nodes		Throughput (Mbps)
	NodeMaps	RL-RF	CDN-P2P	Proposed
50	300	320	340	370
100	280	310	330	365
150	250	290	320	355
200	200	260	300	340

and Figure 3 show that our proposed framework achieves the highest throughput performance, reaching 340 Mbps at 200 nodes. NodeMaps exhibits a throughput of 200 Mbps, while RL-RF and CDN-P2P exhibit medium performance. The SDN controller’s intelligent allocation of bandwidth, combined with the blockchain layer’s enforcement of a peer’s active participation within our framework, ensures reliable data delivery, resulting in throughput gains.

Throughput improvements (up to +70% compared to NodeMaps) are achieved through better load balancing, combined with authenticated peer selection, which eliminates wasted transmissions by free riders. CDN–P2P approaches perform similarly but begin to drop off at scale due to their failure to enforce dynamic trust. Ensuring fair and authenticated transmission environments could be the key to driving throughput in P2P streaming, not only increasing security.

4.2.3. Authentication Accuracy (%)

Authentication accuracy quantifies the effectiveness of the blockchain-based peer verification system. Higher values indicate stronger security and more reliable access control, reducing the risk of unauthorized participation.

$$\mathcal{A}a=\left({\displaystyle \frac{{\mathcal{N}}_{validauthenticatedpeers}}{{\mathcal{N}}_{totalauthenticationattempts}}}\right)\times 100$$

where ${\mathcal{N}}_{validauthenticatedpeers}$ is the number of peers correctly authenticated, ${\mathcal{N}}_{totalauthenticationattempts}$ is the total peer authentication attempts, and $\mathcal{A}a$ is the authentication accuracy.

The results of authentication accuracy, as shown in

Table 8. Numerical outcomes of authentication accuracy (%).

Number of Peer Nodes		Authentication Accuracy (%)
	NodeMaps	RL-RF	CDN-P2P	Proposed
50	64	75	83	95
100	68	79	86	96
150	72	81	88	97
200	75	83	90	98

and Figure 4, demonstrate the advantages of the proposed framework. The system achieves up to 98% accuracy at 200 peers, compared with NodeMaps (75%), RL-RF (83%), and CDN-P2P (90%). The benefit of blockchain is to allow for an immutable verification of a peer’s identity via smart contracts, which can limit unauthorized access as the network expands.

Our authenticated accuracy of 98% improved from the baseline due to the use of smart contract-based peer verification, which ensured only authenticated nodes were selected to participate in forwarding. RL-RF and CDN-P2P provide some authentication; however, both have less accurate authentication because no cryptographic identity management is in place, and consequently, potential impersonation and packet injection are possible. Therefore, the increased authenticated accuracy can be directly related to decreased packet loss and increased delivery ratios in our framework.

4.2.4. Packet Loss Ratio (%)

Packet loss ratio ($\mathcal{P}\mathcal{L}\mathcal{R}$) reflects the percentage of packets that were lost in transit. The lower the packet loss, the more reliable the connection, and thus, improved video playback in P2P networks.

$$\mathcal{P}\mathcal{L}\mathcal{R}=\left({\displaystyle \frac{{\mathcal{P}}_{sent}-{\mathcal{P}}_{received}}{{\mathcal{P}}_{sent}}}\right)\times 100$$

where ${\mathcal{P}}_{sent}$ represents the total number of packets sent and ${\mathcal{P}}_{received}$ represents the total number of packets received.

Packet loss for different numbers of peers is shown in

Table 9. Numerical outcomes of packet loss ratio (%).

Number of Peer Nodes		Packet Loss Ratio (%)
	NodeMaps	RL-RF	CDN-P2P	Proposed
50	4.8	4.2	3.7	0.8
100	6.2	5.5	4.3	1.5
150	7.9	7.1	6.4	1.9
200	9.4	8.7	7.5	2.2

and Figure 5. The proposed approach exhibits overall lower packet loss, with only 2.2% at 200 nodes, compared with NodeMaps, which have a packet loss of 9.4%. The superior performance can be attributed to the fact that SDN-controlled adaptive routing and blockchain enforce appropriate peer behavior, thereby mitigating malicious behavior and resulting in more reliable performance.

4.2.5. Packet Delivery Ratio (%)

Packet Delivery Ratio ($\mathcal{P}\mathcal{D}\mathcal{R})$ reflects the rate of packet delivery success as a percentage. A larger PDR indicates a more reliable potential for a network to operate efficiently and reliably, delivering content regularly.

$$\mathcal{P}\mathcal{D}\mathcal{R}=\left({\displaystyle \frac{{\mathcal{P}}_{received}}{{\mathcal{P}}_{sent}}}\right)\times 100$$

As presented in

Table 10. Numerical outcomes of packet delivery ratio (%).

Number of Peer Nodes		Packet Delivery Ratio (%)
	NodeMaps	RL-RF	CDN-P2P	Proposed
50	80.4	86.9	91.1	95.1
100	84.5	89.8	92.4	96.2
150	88.6	92.1	94.3	97.0
200	91.2	93.7	95.5	97.8

and Figure 6, the proposed framework achieves the highest packet delivery ratio of 97.8% at 200 peers, outperforming all other comparative models. The combination of SDN’s ability to manage traffic in real-time and the secure peer interactions enabled by blockchain results in consistent and high-quality content delivery.

The packet loss ratio falls to 2.2%, a 76% improvement over NodeMaps. This reduction results from trust-enforced routing, which excludes malicious or idle nodes. As a result, PDR increases to 97.8%. RL-RF and CDN-P2P record a higher loss rate because they lack fairness enforcement, allowing selfish peers to degrade delivery. The strong correlation between authentication accuracy and PDR highlights the value of blockchain-based fairness in stabilizing streaming quality.

4.2.6. Control Overhead (%)

Control overhead refers to the incremental signaling effort required for managing the network, including blockchain and SDN management. A minimal amount of overhead is ideal, and we ensure that measures to improve security and enhance control do not lead to inefficiency in management.

$$Controloverhead=\left({\displaystyle \frac{C_{controlmessages}}{C_{totalnetworktraffic}}}\right)\times 100$$

where $C_{controlmessages}$ is the total number of control signal messages exchanged and $C_{totalnetworktraffic}$ is the total network traffic, including data and control messages.

Control overhead comparisons are displayed in

Table 11. Numerical outcomes of control overhead (%).

Number of Peer Nodes		Control Overhead (%)
	NodeMaps	RL-RF	CDN-P2P	Proposed
50	5.2	6.0	7.1	8.1
100	5.7	6.5	7.6	8.4
150	6.3	7.2	8.1	8.8
200	7.0	7.8	8.5	9.3

and Figure 7. Although the suggested work incurs a small increase in control overhead (9.3% with 200 peers), it is acceptable given the comparatively high benefits in terms of security, trust, and performance. The control overhead mainly arises from blockchain operations; however, it stays within acceptable limits and helps support the trust and security of the proposed system.

While the control overhead for this design is higher (9.3%) compared to the baselines (7–8.5%), this is somewhat expected due to the additional signaling required for blockchain operations. Nonetheless, it is worth noting the sublinear increase in overhead as the number of peers in the system increases enough to warrant attention to the overhead consequences of trust-enforced routing. Ultimately, this is not a problematic trade-off, as a 1–2% extra overhead is reasonable in exchange for improvements that yield substantial gains in latency reductions, increased throughput, and enhanced fairness for real-time P2P video streaming.

4.2.7. Confidence Interval

To ensure statistical reliability, each scenario of the simulations was executed 20 times, and averages are reported. The 95% confidence intervals were computed for all performance metrics (i.e., latency, throughput, PDR, packet loss, authentication accuracy, and control overhead). The intervals were consistently tight (±2–4% of the mean), confirming that the improvements demonstrated are statistically significant and not due to random variation. Figure 8 illustrates the confidence interval.

In addition, we examined the performance of our framework against three representative baselines: NodeMaps, which represents decentralization metrics; the reinforcement learning-based routing framework (RL-RF), which represents optimization-based routing; and content delivery network-P2P (CDN-P2P), a representative of hybrid content delivery models.

The simulation results (Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11) demonstrate that our proposed system consistently outperforms the three baseline models across six key metrics. For example, utilizing 200 peers, we can deliver latency at 140 ms (compared with 240 ms for NodeMaps), throughput at 340 Mbps (compared with 250 Mbps for NodeMaps), with authentication accuracy of 98% (compared with 75% for NodeMaps), and delivery ratio of 97.8% (compared with 91.2% for NodeMaps). The control overhead is higher at 9.3% but is still acceptable considering the performance and security gains. An overview of 200 peers is shown in

Table 12. Comparison of the proposed model vs. existing models.

Metric	NodeMaps	RL-RF	CDN-P2P	Proposed (DSDN + Blockchain)
Latency (ms)	240	210	190	140
Throughput (Mbps)	200	260	300	340
Authentication Accuracy (%)	75	83	90	98
Packet Loss (%)	9.4	8.7	7.5	2.2
Packet Delivery Ratio (%)	91.2	93.7	95.5	97.8
Control Overhead (%)	7.0	7.8	8.5	9.3

.

These findings demonstrate that integrating DSDN and blockchain enhances performance and provides a robust trust mechanism that is not present in P2P streaming solutions.

4.3. Quantitative Comparison with Prior Integrated SDN–Blockchain Approaches

Comparing our work with recent integrated SDN–blockchain systems published in [28,29,30,31,32] allowed us to emphasize its significance further. In addition to reporting quantifiable costs, these methods mainly focus on IoT security, edge intelligence, and cross-domain traffic engineering. GATE-BC [28], for instance, demonstrated notable gains in dynamic resource allocation across domains; however, it incurred a 25–40% higher signaling overhead compared with static optimization, similar to the system in [30] integrated SDN, blockchain, and fuzzy neural networks for threat monitoring. However, due to multi-layer inspection, this placed a significant computational burden (~70% CPU usage) on edge devices. Blockchain-induced delays and scalability issues were acknowledged by [31] and CITE-PSO [29], but they also talked about advances in coordination and pricing fairness. Investigating AI-driven SDN–blockchain integration for Internet of Things (IoT) and cyber–physical systems (CPS) security, Ref. [32] did not provide quantifiable QoS metrics in streaming scenarios.

In comparison, our suggested architecture maintains a low control overhead of 9.3% at 200 peers, while achieving complete end-to-end QoS improvements in P2P streaming. This overhead avoids the significant CPU strain (~70%) observed in [30] and is 2.7×–4.3× lower than the 25–40% signaling cost estimated for GATE-BC. Furthermore, compared with previous integrated research [28,29,30,31,32], our system offers measurable improvements in streaming, including a packet delivery ratio of 97.8%, a 41.7% reduction in latency, a 70% increase in throughput, and a 98% authentication accuracy. These comparisons verify that previous SDN–blockchain hybrids had conceptual value, but they either had streaming-oriented assessments or imposed significant system costs. Our architecture, on the other hand, strikes a compromise between scalability and trust enforcement, providing real-time multimedia applications with both measurable performance gains and reduced operational costs.

We assessed the security robustness of the proposed framework, in addition to its performance measures. The effectiveness of authentication was measured by false rejection rate (FRR) and false acceptance rate (FAR). The findings indicate that smart contract-based verification minimizes false peer exclusions while offering excellent authentication accuracy, with an FAR of 1.5% and an FRR of 0.7%.

The blockchain ledger limits Sybil amplification by preventing multiple registrations of the same cryptographic identity, thereby thwarting Sybil attacks, in which adversaries attempt to overload the network with fake identities. The distributed SDN controller design eliminates isolation by a small set of malicious nodes and enforces multipath peer selection, hence mitigating eclipse attacks that manipulate connections to isolate nodes. Rate-limiting rules can be dynamically deployed through the SDN plane to slow suspicious flows. The separation of control and data planes in DSDN ensures that traffic floods targeting one controller do not jeopardize the entire system in the event of a DDoS attack. Collectively, our analyses verify that resilience against significant P2P risks is offered by combining distributed SDN routing with blockchain-based identity management.

5. Discussion

The comparative analysis reveals that DSDN, combined with blockchain, resulted in a 41.7% reduction in end-to-end latency (from 140 ms to 240 ms in NodeMaps) and achieved an authentication validation accuracy of 98% compared to 75% in NodeMaps. The quantitative advantage of the joint with additivity indicates improved reliability and security in large-scale P2P streaming media networks. The introduced framework consistently outperformed the other approaches, including NodeMaps, RL-RF, and CDN-P2P, in terms of latency, throughput, authentication accuracy, and packet delivery reliability to peers.

These improvements were made possible by DSDN, which distributes the traffic control logic across multiple controllers, enabling real-time decisions and dynamic bandwidth optimization without relying on a single centralized point of control. Meanwhile, the blockchain component offered decentralized and tamper-resistant peer authentication and trust management using smart contracts.

The integration has addressed significant challenges highlighted in previous research, including free-rider behavior, unauthorized peers, and inefficiencies in content delivery. Although control overhead rose slightly to 9.3% at 200 peers (compared with 7–8.5% in baselines), the minimal costs are offset by higher rewards: packet delivery ratio improved to 97.8%, packet loss reduced to 2.2%, and throughput increased to 340 Mbps. This indicates that small signaling costs can be justified in terms of tangible improvements in fairness enforcement and robustness.

The hybrid framework supports high-quality video streaming due to its ability to maintain stable delivery in the presence of peer churn. Latency was less than 150 ms, while throughput was consistently over 340 Mbps, even at 200 peers; thus, smooth playback was maintained even under challenging conditions. The framework leverages DSDN’s distributed control capabilities to adaptively manage traffic and routing, improving access and utilization of available bandwidth. At the same time, blockchain is employed to validate peer behavior through a decentralized trust structure that ensures fairness and accountability.

Although security and fairness were key design motivations, their direct evaluation, especially under attack conditions, was not the focus of this study and is recommended for future exploration. Moreover, future efforts may include testing the framework on a larger-scale real-world testbed and incorporating resilience scenarios involving controller failures or malicious nodes.

This work addresses a significant gap in current research, where SDN and blockchain are often examined in isolation. The integration presented here offers a cohesive solution that balances scalability, effectiveness, and trust in decentralized multimedia content delivery. In conclusion, the research goals of the study have been met by demonstrating that distributed control (DSDN) using blockchain-based trust mechanisms can meet both performance and fairness. Optimizing both performance and fairness enhances the value of P2P streaming systems far beyond SDN-only or blockchain-only mechanisms.

The originality of this study stems from the hybrid approach of combining the distributed SDN (DSDN) and blockchain for P2P video streaming. Previous work focused either on optimally delivering network performance through SDN or increasing trust in the streaming system through blockchain. Our goal was to simultaneously address both performance and trust in response to P2P streaming using DSDN and blockchain technology, ultimately allowing for low-latency, high-throughput, and fairness among peers. Our innovative framework focuses on high-bandwidth, real-time P2P multimedia streaming, contextualizing its contributions in relation to other related works on SDN–blockchain integration in IoT networks and the inter-ISP network context. Our comparative study against NodeMaps, RL-RF, and CDN-P2P demonstrates that the proposed system, operating at equivalent control overhead levels, performed better than previous state-of-the-art implementations in at least six key areas, illustrating a wholly original approach potentially applicable to next-generation streaming systems.

The key points of differentiation are as follows:

• Application of DSDN + Blockchain to P2P streaming:

  ➢

  Prior related SDN-centric literature improves traffic optimization, yet often relies on centralized controllers, which creates single points of failure and inhibits resiliency.

  ➢

  Various blockchain-based solutions improve trust but introduce latency overhead which is not suitable for real-time streaming.

  ➢

  Our framework uniquely bridges DSDN’s distributed and adaptive traffic management with blockchain’s decentralized trust enforcement, providing a patented solution for real-time multimedia streaming.

• Trust enforcement and free-rider mitigation:

  ➢

  Current blockchain models are primarily focused on data integrity and payment models (e.g., Ethereum/IPFS micro-payments), without addressing fairness among peers.

  ➢

  We propose a method for tracking peer contributions through smart contracts to ensure fairness and limit free riding, which has been ignored in other P2P streaming literature.

• Comprehensive comparative evaluation:

  ➢

  In contrast to previous research that concentrated on a single performance dimension (e.g., bandwidth, decentralization, energy, or caching), our framework is assessed based on six essential metrics: latency, throughput, authentication accuracy, packet delivery ratio, packet loss ratio, and control overhead.

  ➢

  Simulation with 200 peers demonstrates continuous improvement to state-of-the-art benchmarks (NodeMaps, RL-RF, CDN-P2P) in both performance and security.

• Lightweight, scalable design:

  ➢

  Previous SDN–blockchain integrations (e.g., GATE-BC, CITE-PSO) demonstrate potential, but either result in excessive signaling overhead or lack scalability due to churn.

  ➢

  We utilize distributed controllers with a private blockchain to mitigate centralization risks, while keeping control overhead reasonable at 9.3%, thereby balancing trust and scalability.

• Domain-specific innovation for multimedia streaming:

  ➢

  To the best of our knowledge, this is the first framework designed explicitly for P2P video streaming that exploits the SDN–blockchain interplay.

  ➢

  It provides solutions to real multimedia problems such as real-time latency, packet loss, and peer churn, which are lacking in previous work or viewed as tangential.

According to our data, the framework grows well up to 200 peers, with control overhead increasing only sublinearly (9.3%) and latency and throughput improving. According to this pattern, even with thousands of peers, overhead should be approximately 15% as distributed SDN controllers and blockchain contracts steer clear of central bottlenecks. Using lightweight consensus protocols such as PoA and Delegated Proof of Stake (DPoS), batching authentication events, off-chain verification, and GPU-accelerated elliptic curve encryption can all increase performance. These improvements would save expenses while preserving equity and upholding confidence in extensive implementations.

Future research will use Ethereum (PoA) on distributed SDN controllers in a real-world testbed deployment, despite our assessment being simulation-based. This will enable validation in real-world P2P systems, which frequently experience significant peer churn and fluctuating network circumstances. The lack of extensive field testing and simplified gas and delay models are among the current drawbacks. By addressing these issues, the framework will be more prepared for implementation in IoT and live streaming scenarios.

6. Conclusions

This study validated the effectiveness of the DSDN–blockchain hybrid framework, which achieved a latency reduction of up to 41.7%, a 70% improvement in throughput, and a nearly 0% error rate (98% accuracy) in authentication. These results suggest that the proposed architecture supports low-latency routing and provides zero-knowledge, secure and fair participation in P2P video streaming. With simulation, we consistently achieved improvements over benchmarks (NodeMaps, RL-RF, CDN-P2P) in all measures (latency, throughput, authentication accuracy, packet delivery ratio, and packet loss ratio), with acceptable increases in control overhead. This study is novel in that it addresses optimization and decentralized trust at the same time: distributed SDN offers adaptive traffic control capabilities, and blockchain provides (with acceptably low latencies) fairness and peer accountability. To the best of our knowledge, this is the first framework specifically designed for real-time P2P multimedia delivery that utilizes a distributed SDN and blockchain-based smart contracts and is the first to offer a unique solution to the dual challenges of scalability and trust.

In addition to making contributions to academic literature, the framework also has practical application potential in real-time video streaming, distance education, and virtual or augmented reality applications that require a latency of less than 150 ms, as well as fair resource sharing. It can be leveraged for decentralized live video broadcasting platforms that manage fairness and reliability for significant events. In distance learning and virtual classrooms, the framework will function to maintain reliable and secure video delivery in volatile environments with high peer churn. In emerging immersive application domains (for example, VR and AR), DSDN and blockchain should reduce latency while ensuring protection against malicious peers to prevent degradation of the quality of service. Additionally, this decentralized architecture may offer its benefits to disaster recovery and emergency communications networks, as it can provide trust without relying on centralized infrastructure. This framework can also be extended to the IoT multimedia streaming domain (e.g., surveillance, smart cities), where real-time video data must be securely transmitted among devices constrained by resources.

In the future, it is recommended to extend simulation by running it on real-world testbeds with significant peer churn, studying lightweight consensus protocols (e.g., PoA, DPoS), and GPU-optimized ECC to minimize control costs even further while preserving fairness and scalability. These extensions will expand the framework’s use in domains that demand scalable, secure, and low-latency streaming.