Video Streaming Service Solutions

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 777

Special Issue Editors


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Guest Editor
Department of Telecommunications and ICT, Wroclaw University of Science and Technology, 50-370 Wrocław, Poland
Interests: service quality; objective and subjective quality assessments; multimedia streaming; teletraffic engineering; network dimensioning

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Guest Editor
Department of Acoustics, Multimedia and Signal Processing, Faculty of Electronic, Photonics and Microsystems, Wroclaw University of Science and Technology, 50-370 Wrocław, Poland
Interests: audio and video quality assessment; audio and video coding; crime acoustics; digital signal processing

E-Mail Website
Guest Editor
Department of Information and Communication, Flensburg University of Applied Sciences, 24943 Flensburg, Germany
Interests: communication networks; performance analysis of queuing systems; Internet technology; quality of service
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Special Issue Information

Dear Colleagues,

Currently, video generates more than 80% of internet traffic and is an information medium on which many services are based. One of the main ways of delivering content on IP networks is video streaming, whose condition has a significant impact on the quality of the services offered.

Parameters such as network throughput, delays, and jitter are the basic factors that should be controlled because they have a direct impact on the quality of the streaming service.

They become particularly important when talking about the implementation of real-time services.

Moreover, various unfavorable phenomena that occur in wired and, in particular, wireless packet networks affect packet loss, which reduces the quality of the service provided.

Therefore, there is a need to counteract the unfavorable consequences of the abovementioned phenomena, including by building networks with high bandwidth which are safe and resistant to external interference. On the other hand, there is a need to develop more efficient coding methods, implement new codecs, design new communication protocols, and implement dynamic and adaptive streaming methods. Once all this has been achieved, the next step is to measure and determine the resulting service quality. This can be achieved by using existing or implementing new methods, e.g., methods based on machine learning, for assessing the quality of services.

This Special Issue is devoted to the current and future challenges faced by content and service providers, as well as network operators, to ensure the highest level of quality of services provided to users.

In particular, this Special Issue will be devoted to, but is not limited to, topics such as the following:

  • Video streaming.
  • Development of new streaming techniques.
  • Resource-efficient streaming algorithms.
  • Adaptive dynamic streaming on IP networks.
  • Video codecs and compression.
  • Mobile video streaming applications.
  • Multimedia applications.
  • Streaming protocols.
  • Network performance.
  • Critical network challenges.
  • Using neural networks to improve video streaming performance.
  • Cloud-based video streaming services.
  • Cloud video processing.
  • Video streaming solutions for scalability and cost-effectiveness.
  • Future communication systems.
  • Quality of Service.
  • Quality of Experience.
  • Security issues in wired and wireless networks.
  • Subjective video quality assessments.
  • Objective video quality evaluations.
  • Classical and machine learning-based quality assessment methods.

Dr. Janusz Klink
Dr. Stefan P. Brachmański
Prof. Dr. Tadeus Uhl
Guest Editors

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Keywords

  • communication networks
  • communication protocols
  • performance analysis
  • multimedia services
  • streaming protocols
  • QoE (Quality of Experience)
  • Quality of Service (QoS)
  • UX (User Experience)
  • machine learning

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Published Papers (1 paper)

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Research

24 pages, 19576 KiB  
Article
Evaluating HAS and Low-Latency Streaming Algorithms for Enhanced QoE
by Syed Uddin, Michał Grega, Mikołaj Leszczuk and Waqas ur Rahman
Electronics 2025, 14(13), 2587; https://doi.org/10.3390/electronics14132587 - 26 Jun 2025
Viewed by 394
Abstract
The demand for multimedia traffic over the Internet is exponentially growing. HTTP adaptive streaming (HAS) is the leading video delivery system that delivers high-quality video to the end user. The adaptive bitrate (ABR) algorithms running on the HTTP client select the highest feasible [...] Read more.
The demand for multimedia traffic over the Internet is exponentially growing. HTTP adaptive streaming (HAS) is the leading video delivery system that delivers high-quality video to the end user. The adaptive bitrate (ABR) algorithms running on the HTTP client select the highest feasible video quality by adjusting the quality according to the fluctuating network conditions. Recently, low-latency ABR algorithms have been introduced to reduce the end-to-end latency commonly experienced in HAS. However, a comprehensive study of the low-latency algorithms remains limited. This paper investigates the effectiveness of low-latency streaming algorithms in maintaining a high quality of experience (QoE) while minimizing playback delay. We evaluate these algorithms in the context of both Dynamic Adaptive Streaming over HTTP (DASH) and the Common Media Application Format (CMAF), with a particular focus on the impact of chunked encoding and transfer mechanisms on the QoE. We perform both objective as well as subjective evaluations of low-latency algorithms and compare their performance with traditional DASH-based ABR algorithms across multiple QoE metrics, various network conditions, and diverse content types. The results demonstrate that low-latency algorithms consistently deliver high video quality across various content types and network conditions, whereas the performance of the traditional adaptive bitrate (ABR) algorithms exhibit performance variability under fluctuating network conditions and diverse content characteristics. Although traditional ABR algorithms download higher-quality segments in stable network environments, their effectiveness significantly declines under unstable conditions. Furthermore, the low-latency algorithms maintained high user experience regardless of segment duration. In contrast, the performance of traditional algorithms varied significantly with changes in segment duration. In summary, the results underscore that no single algorithm consistently achieves optimal performance across all experimental conditions. Performance varies depending on network stability, content characteristics, and segment duration, highlighting the need for adaptive strategies that can dynamically respond to varying streaming environments. Full article
(This article belongs to the Special Issue Video Streaming Service Solutions)
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