DeepVR: Deep Reinforcement Learning for Predictive Panoramic Video Streaming

Abstract

Online panoramic video has recently gained enormous popularity. Tile-based adaptive streaming is a promising paradigm to deliver a panoramic video for the sake of bandwidth saving. Nevertheless, it is challenging to accurately predict user’s field of view (FoV) and deliver the optimal bitrate due to the dynamic user behavior and time-varying network conditions. In this paper, we propose a novel approach of deep reinforcement learning based predictive panoramic video delivering. Specifically, a carefully-devised long short-term memory (LSTM) model is used to predict the FoV in the next few seconds. Our quality adaptation policy is based on a deep reinforcement learning (DRL) agent, which is able to intelligently adapt its bitrate selection policy tailored to the dynamic environments. To validate the effectiveness we have implemented a prototype of this system. With the integrated DRL algorithm Rainbow, we have achieved a superior performance in terms of the quality of experience (QoE) score, which outperforms existing panoramic video streaming frameworks.