MA360: Multi-Agent Deep Reinforcement Learning Based Live 360-Degree Video Streaming on Edge

Abstract

The mobile edge caching has made video service providers deliver live 360-degree videos worldwide. However, these services still suffer from the huge network traffic on the core network due to the spherical nature and the diverse requests generated from large user populations. It is challenging to optimize the Quality of Experience (QoE) and the bandwidth consumption simultaneously under the significant number of users as well as dynamic network and playback status. In this paper, we propose a Multi-Agent deep reinforcement learning based 360-degree video streaming system, named MA360, to tackle this multi-user live 360-degree video streaming problem in the context of the edge cache network. Specifically, MA360 employs the Mean Field Actor-Critic (MFAC) algorithm to make clients collaboratively and distributively request tiles aiming at maximizing the overall QoE while minimizing the total bandwidth consumption. Experiments over real-world datasets show that MA360 can improve the QoE while significantly reducing the bandwidth consumption compared with several state-of-the-art edge-assisted 360-degree video streaming strategies.