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

Autor:	Ren-Hung Hwang, Po-Yu Chou, Wen-Tsuen Chen, Wei-Yu Chen, Chih-Yu Wang
Rok vydání:	2020
Předmět:	Optimization problem Mobile edge computing Computer science Distributed computing Reinforcement learning Resource management Markov decision process Quality of experience Video quality Integer programming
Zdroj:	ICC
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::de8417061ed84868843fd777fdd9966a https://doi.org/10.1109/icc40277.2020.9149086 Zobrazit plný text záznamu