Cross Layer Optimization and Distributed Reinforcement Learning Approach for Tile-Based 360 Degree Wireless Video Streaming

• URL: http://arxiv.org/abs/2011.06356v1
• Date: Thu, 12 Nov 2020 12:59:10 GMT
• Title: Cross Layer Optimization and Distributed Reinforcement Learning Approach for Tile-Based 360 Degree Wireless Video Streaming
• Authors: Mounssif Krouka, Anis Elgabli, Mohammed S. Elbamby, Cristina Perfecto, Mehdi Bennis, Vaneet Aggarwal
• Abstract summary: We show that the problem can be decoupled into two interrelated subproblems. We prove that the physical layer subproblem can be solved optimally with low complexity. An actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents.
• Score: 63.14489142588682
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the streaming algorithm at hand should maximize the average quality of experience (QoE) while guaranteeing a minimum rate for each user. In this paper, we propose a \emph{cross layer} optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. Particularly, we consider a tile based 360 degree video streaming, and we optimize a QoE metric that balances the tradeoff between maximizing each user's QoE and ensuring fairness among users. We show that the problem can be decoupled into two interrelated subproblems: (i) a physical layer subproblem whose objective is to find the download rate for each user, and (ii) an application layer subproblem whose objective is to use that rate to find a quality decision per tile such that the user's QoE is maximized. We prove that the physical layer subproblem can be solved optimally with low complexity and an actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem. Extensive experiments reveal the robustness of our scheme and demonstrate its significant performance improvement compared to several baseline algorithms.

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