Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

The 360-degree video streaming has higher bandwidth requirements compared with traditional video to achieve the same user-perceived playback quality. Since users only view part of the entire videos, viewport-adaptive streaming is an effective approach to guarantee video quality. However, the performance of viewport-adaptive schemes is highly dependent on the bandwidth estimation and viewport prediction. To overcome these issues, we propose a novel reinforcement learning (RL) based viewport-adaptive streaming framework called RLVA, which optimizes the 360-degree video streaming in viewport prediction, prefetch scheduling and rate adaptation. Firstly, RLVA adopts t location-scale distribution rather than Gaussian distribution to describe the viewport prediction error characteristic more accurately and achieve the tile viewing probability based on the distribution. Besides, a tile prefetch scheduling algorithm is proposed to update the tiles according to the latest prediction results, which further reduces the adverse effect of prediction error. Furthermore, the tile viewing probabilities are treated as input status of RL algorithm. In this way, RL can adjust its policy to adapt to both of the network conditions and viewport prediction error. Through extensive evaluations, the simulation results show that the proposed RLVA outperforms other viewport-adaptive methods by about 4.8%-66.8% improvement of Quality of Experience (QoE) and effectively reduces the impact of viewport prediction errors.