Cooperative 360° Video Delivery Network: A Multi-Agent Reinforcement Learning Approach

Abstract

With the stringent requirement of receiving video from unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically cluster APs into virtual cells for a different group of VR users with overlapped video requests. We first introduce the conventional non-learning-based association algorithms. We then formulate the association problem into a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. Our simulation results shown that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architectures is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.