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With the stringent requirement of receiving video from unmanned aerialvehicle (UAV) from anywhere in the stadium of sports events and thesignificant-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 explorethe benefit of broadcasting user-correlated decode-dependent video resources tospatially correlated VR users, the network should dynamically schedule thevideo and cluster APs into virtual cells for a different group of VR users withoverlapped video requests. By decomposition the problem into scheduling andassociation sub-problems, we first introduce the conventionalnon-learning-based scheduling and association algorithms, and a centralizeddeep reinforcement learning (DRL) association approach based on the rainbowagent with a convolutional neural network (CNN) to generate decisions fromobservation. To reduce its complexity, we then decompose the associationproblem into multiple sub-problems, resulting in a networked-distributedPartially Observable Markov decision process (ND-POMDP). To solve it, wepropose a multi-agent deep DRL algorithm. To jointly solve the coupledassociation and scheduling problems, we further develop a hierarchicalfederated DRL algorithm with scheduler as meta-controller, and association asthe controller. Our simulation results shown that our CF-MB network caneffectively handle real-time video transmission from UAVs to VR users. Ourproposed learning architectures is effective and scalable for ahigh-dimensional cooperative association problem with increasing APs and VRusers. Also, our proposed algorithms outperform non-learning based methods withsignificant performance improvement.

Correlation-aware Cooperative Multigroup Broadcast 360{\deg} Video Delivery Network: A Hierarchical Deep Reinforcement Learning Approach