Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
Round 1
Reviewer 1 Report
Please refer to the attached pdf file.
Comments for author File: 
Comments.pdf
Author Response
Please see attached.
Author Response File: Author Response.pdf
Reviewer 2 Report
Paper: Learning-based Online QoE Optimization in Multi-Agent Video Streaming
My comments are below:
1. Please revise the Journal format. Numbering for sections must not begin in zero.
2. In the introduction section, please avoid the usage of lumped references. It must be required that each contribution per reference can be clearly stated in the paper.
3. Section 2 regarding system modeling can be improved by including a complete nomenclature list following the Journal rules.
4. Evaluation section and results are well presented.
5. Conclusions must be improved by including some numerical achievements. In addition, it is recommended to include two or three possible future works.
Author Response
Please see attached.
Author Response File: Author Response.pdf
Reviewer 3 Report
The manuscript is interesting and important. It needs revisions before publication-
- The abstract is very basic; even it consists of confusion matrix parameters. If possible.
- How the performance of proposed methodology is better than existing classifiers/detectors? Author must explain.
- More explanation on results and tables is expected.
- Avoid the use of “I and We” in the manuscript.
- The English and typo errors of the paper should be checked in the presence of native English speaker.
- Author should add pros and cons of the proposed technique.
- Author should revise the conclusion by adding future scope and important findings of the paper.
- Author should explain all associated symbols in all equations.
- Author should add future scope of the paper.
- The following references must be cited in the paper. These are based on different classifiers/detectors-
-Automation of noise sampling in deep reinforcement learning
-FERNet: A Deep CNN Architecture for Facial Expression Recognition in the Wild
-A Novel Ultra Wideband Antenna Design and Parameter Tuning Using Hybrid Optimization Strategy
-Elements Failure Detection and Radiation Pattern Correction for Time-Modulated Linear Antenna Arrays Using Particle Swarm Optimization
-A Non-Uniform Circular Antenna Array Failure Correction Using Firefly Algorithm
-Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach
Author Response
Please see attached.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The Authors have effectively addressed all my comments and I think that the overall quality and readability of the manuscript have been improved.
I can thus recommend acceptance.
Reviewer 3 Report
Accepted in current form
