  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 schedule the
video and cluster APs into virtual cells for a different group of VR users with
overlapped video requests. By decomposition the problem into scheduling and
association sub-problems, we first introduce the conventional
non-learning-based scheduling and association algorithms, and a centralized
deep reinforcement learning (DRL) association approach based on the rainbow
agent with a convolutional neural network (CNN) to generate decisions from
observation. To reduce its complexity, we then decompose the association
problem into multiple sub-problems, resulting in a networked-distributed
Partially Observable Markov decision process (ND-POMDP). To solve it, we
propose a multi-agent deep DRL algorithm. To jointly solve the coupled
association and scheduling problems, we further develop a hierarchical
federated DRL algorithm with scheduler as meta-controller, and association as
the controller. 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.
