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 language | English |
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Title of host publication | Proceedings of the 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing |
Publisher | IEEE |
Pages | 4230-4234 |
Number of pages | 5 |
ISBN (Print) | 9781728176055 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 2021 IEEE International Conference on Acoustics, Speech and Signal Processing - , Canada Duration: 6 Jun 2021 → 11 Jun 2021 |
Conference
Conference | 2021 IEEE International Conference on Acoustics, Speech and Signal Processing |
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Country/Territory | Canada |
Period | 6/06/21 → 11/06/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- 360° video streaming
- Game theory
- Quality of experience
- Reinforcement learning