4.7 Article

RAPT360: Reinforcement Learning-Based Rate Adaptation for 360-Degree Video Streaming With Adaptive Prediction and Tiling

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2021.3076585

关键词

Streaming media; Quality of experience; Bit rate; Adaptive systems; Prediction algorithms; Adaptation models; Bandwidth; 360-degree videos; tile-based adaptive streaming; adaptive viewport prediction; viewport-aware tiling; rate adaptation; deep reinforcement learning

资金

  1. National Natural Science Foundation of China [61931023, 61831018, 61871267, 61972256, 91838303, 61971285, 61720106001, 61932022]
  2. Program of Shanghai Science and Technology Innovation Project [20511100100]
  3. Shanghai Rising-Star Program [20QA1404600]

向作者/读者索取更多资源

This paper proposes a tile-based rate adaptation strategy called RAPT360 for adaptive 360-degree video streaming. It uses reinforcement learning to dynamically learn the optimal bitrate allocation of tiles, aiming to improve the quality of experience (QoE) under constrained network conditions.
Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users' viewports and optimal bitrate allocation for tiles. In this paper, we propose a strategy that deploys reinforcement learning-based Rate Adaptation with adaptive Prediction and Tiling for 360-degree video streaming, named RAPT360, to address these challenges. Specifically, to improve the accuracy of the state-of-the-art viewport prediction approaches, we fit the time-varying Laplace distribution-based probability density function of the prediction error for different prediction lengths. On the basis of that, we develop a viewport identification method to determine the viewport area of a user depending on the buffer occupancy, where the obtained viewport can cover the real viewport with any given probability confidence level. We then propose a viewport-aware adaptive tiling scheme to improve the bandwidth efficiency, where three types of tile granularities are allocated according to the shape and position of the 2-D projection of that viewport. By establishing an adaptive streaming model and QoE metric specific to 360-degree videos, we finally formulate the rate adaptation problem for tile-based 360-degree video streaming as a non-linear discrete optimization problem that targets at maximizing the long-term user QoE under a bandwidth-constrained network. To efficiently solve this problem, we model the rate adaptation logic as a Markov decision process (MDP) and employ the deep reinforcement learning (DRL)-based algorithm to dynamically learn the optimal bitrate allocation of tiles. Extensive experimental results show that RAPT360 achieves a performance gain of at least 1.47 dB on average chunk QoE, including a video quality improvement of at least 1.33 dB, in comparison to the existing strategies for tile-based adaptive 360-degree video streaming.

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