Deep Reinforcement Learning-based Rate Adaptation for Adaptive 360-Degree Video Streaming

Nuowen Kan; Junni Zou; Kexin Tang; Chenglin Li; Ning Liu; Hongkai Xiong

ICASSP 2019accepted0 citations

Deep Reinforcement Learning-based Rate Adaptation for Adaptive 360-Degree Video Streaming

Nuowen Kan, Junni Zou, Kexin Tang, Chenglin Li, Ning Liu, Hongkai Xiong

Abstract

In this paper, we propose a deep reinforcement learning (DRL)-based rate adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to reduce the possible switching latency of the field of view (FoV), we design a new QoE metric by introducing a penalty term for the large buffer occupancy. A scalable FoV method is further proposed to alleviate the combinatorial explosion of the action space in the DRL formulation. Then, we model the rate adaptation logic as a Markov decision process and employ the DRL-based algorithm to dynamically learn the optimal video transmission rate. Simulation results show the superior performance of the proposed algorithm compared to the existing algorithms.

BibTeX

@inproceedings{icassp2019_deepreinforcemen,
  title = {Deep Reinforcement Learning-based Rate Adaptation for Adaptive 360-Degree Video Streaming},
  author = {Nuowen Kan and Junni Zou and Kexin Tang and Chenglin Li and Ning Liu and Hongkai Xiong},
  booktitle = {ICASSP 2019},
  year = {2019}
}