Deep Reinforcement Learning Based Adaptive 360-degree Video Streaming with Field of View Joint Prediction

Abstract

With the development of 360-degree video and HTTP adaptive streaming (HAS), tile-based adaptive 360-degree video streaming has become a promising paradigm for reducing the bandwidth consumption of delivering the panoramic video content. However, there are two main challenges for the adaptive 360-degree video streaming, accurate long-term prediction of the future field of view (Fo V) and optimal adaptive bitrate (ABR) transmission strategy. In this paper, we propose an attention-based multi-user Fo V joint prediction approach to improve the accuracy, establishing a probability model of watching video tiles for users and applying Long Short-Term Memory (LSTM) network and DBSCAN clustering method. Furthermore, we present an adaptive 360-degree video streaming approach based on deep reinforcement learning (DRL), using A3C algorithm to optimize the QoE. The real-world trace-driven experiments demonstrate that our approach achieves about 8 % gains on user Fo V prediction precision and an increase at least 20 % on user QoE compared with the benchmarks.