Perceptual Quality Aware Adaptive 360-Degree Video Streaming with Deep Reinforcement Learning

As 360-degree videos are with high data volume, it is a great challenge to deliver the content and provide a high quality of experience (QoE) for users. In this paper, we investigate the tile-level rate allocation problem with the purpose of optimizing users’ QoE. Specifically, we find the nonlinearity between video quality and bitrate through extensive experiments. Thus, a QoE metric is defined to better measure the perceptual quality. Considering the sequential decision nature of video streaming, we formulate the rate decision problem as an Markov Decision Process. Then we propose a deep reinforcement learning based rate adaptive streaming approach to solve this problem. However, the solution space is large as a 360-degree video is spatially partitioned into multiple tiles. In order to address the problem of combinatorial explosion, we propose a tile classification method based on the predicted viewpoint. Experimental results based on real-world traces show that our algorithm can improve the overall QoE by 16% − 22% compared to existing algorithms.