Joint reinforcement learning and game theory bitrate control method for 360-degree dynamic adaptive streaming

Xuekai WEI, Mingliang ZHOU, Sam KWONG, Hui YUAN, Tao XIANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

4 Citations (Scopus)

Abstract

A joint reinforcement learning (RL) and game theory method is presented for segment-level continuous bitrate selection and tile-level bitrate allocation in tile-based 360-degree streaming to increase users' quality of experience (QoE). First, a viewpoint prediction method based on single-user (SU) viewpoint traces and the saliency map (SM) model is presented to model viewing behaviours. Second, an RL method is proposed to predict segment bitrate and a cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for every tile with the help of the viewpoint prediction map. Performance evaluation results indicate that the proposed method can outperform the state-of-the-art methods in terms of different QoE objectives.
Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
Pages4230-4234
ISBN (Print)9781728176055
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech and Signal Processing - , Canada
Duration: 6 Jun 202111 Jun 2021

Conference

Conference2021 IEEE International Conference on Acoustics, Speech and Signal Processing
Country/TerritoryCanada
Period6/06/2111/06/21

Keywords

  • 360° video streaming
  • Game theory
  • Quality of experience
  • Reinforcement learning

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