Multi-User Adaptive Video Delivery over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

Kexin Tang, Nuowen Kan, Junni Zou, Chenglin Li, Xiao Fu, Mingyi Hong, Hongkai Xiong

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users' long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.

Original languageEnglish (US)
Article number9035396
Pages (from-to)798-815
Number of pages18
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume31
Issue number2
DOIs
StatePublished - Feb 2021

Bibliographical note

Funding Information:
Manuscript received November 6, 2019; revised January 26, 2020 and February 28, 2020; accepted March 2, 2020. Date of publication March 13, 2020; date of current version February 4, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61831018, Grant 61931023, Grant 61871267, Grant 61972256, and Grant 61720106001, and in part by the China Scholarship Council. The work of Mingyi Hong was supported in part by the National Science Foundation under Grant CIF-1910385 and in part by the Army Research Office under Grant 73202-CS. This article was recommended by Associate Editor C. Wu. (Corresponding author: Chenglin Li.) Kexin Tang, Nuowen Kan, Junni Zou, Chenglin Li, and Hongkai Xiong are with the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: tkx1994-china@sjtu.edu.cn; kannw_1230@sjtu.edu.cn; zou-jn@cs.sjtu. edu.cn; lcl1985@sjtu.edu.cn; xionghongkai@sjtu.edu.cn).

Publisher Copyright:
© 1991-2012 IEEE.

Keywords

  • Wireless video streaming
  • beamforming
  • cross-layer design
  • rate adaptation
  • reinforcement learning

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