Deep Reinforcement Learning for MEC Streaming with Joint User Association and Resource Management

Po Yu Chou, Wei Yu Chen, Chih Yu Wang, Ren Hung Hwang, Wen Tsuen Chen

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we first reformulate this problem as a Markov Decision Process (MDP) and develop a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement especially in the low wireless resource and high user number scenario compared to other baselines.

原文English
主出版物標題2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728150895
DOIs
出版狀態Published - 6月 2020
事件2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
持續時間: 7 6月 202011 6月 2020

出版系列

名字IEEE International Conference on Communications
2020-June
ISSN(列印)1550-3607

Conference

Conference2020 IEEE International Conference on Communications, ICC 2020
國家/地區Ireland
城市Dublin
期間7/06/2011/06/20

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