Abstract
A 360-degree streaming system can provide immersive, interactive, and autonomous experiences surrounding the user by means of viewpoint changes to see different angles of a 360-degree video. However, due to the limited capacity and highly dynamic conditions of cellular networks, high-resolution 360-degree video playback over mobile devices often suffers from playback freezing, and bandwidth waste is inevitably incurred in delivering out-of-view video data. In this paper, a hybrid control scheme is presented for segment-level continuous bitrate selection and tile-level bitrate allocation for 360-degree streaming over mobile devices to increase users' quality of experience. First, a deep reinforcement learning (RL) method is proposed to predict the segment bitrate and avoid playback freezing. Second, a viewpoint-prediction-map-based cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for each tile to reduce unreasonable bandwidth waste. The proposed scheme is compared with state-of-the-art approaches under a wide variety of mobile network conditions with multiple viewpoint traces and 360-degree video contents. The experimental results indicate that the proposed method outperforms the compared state-of-the-art approaches in terms of various experimental objectives on mobile devices.
Original language | English |
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Pages (from-to) | 3428-3442 |
Number of pages | 15 |
Journal | IEEE Transactions on Mobile Computing |
Volume | 21 |
Issue number | 10 |
Early online date | 9 Feb 2021 |
DOIs | |
Publication status | Published - 1 Oct 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2002-2012 IEEE.
Keywords
- 360-degree streaming systems
- game theory
- quality of experience
- reinforcement learning