  360-degree videos require significant bandwidth to provide an immersive
viewing experience. Wireless systems using terahertz (THz) frequency band can
meet this high data rate demand. However, self-blockage is a challenge in such
systems. To ensure reliable transmission, this paper explores THz-enabled
360-degree video streaming through multiple multi-antenna access points (APs).
Guaranteeing users' quality of experience (QoE) requires accurate viewport
prediction to determine which video tiles to send, followed by asynchronous
bitrate selection for those tiles and beamforming design at the APs. To address
users' privacy and data heterogeneity, we propose a content-based viewport
prediction framework, wherein users' head movement prediction models are
trained using a personalized federated learning algorithm. To address
asynchronous decision-making for tile bitrates and dynamic THz link
connections, we formulate the optimization of bitrate selection and beamforming
as a macro-action decentralized partially observable Markov decision process
(MacDec-POMDP) problem. To efficiently tackle this problem for multiple users,
we develop two deep reinforcement learning (DRL) algorithms based on
multi-agent actor-critic methods and propose a hierarchical learning framework
to train the actor and critic networks. Experimental results show that our
proposed approach provides a higher QoE when compared with three benchmark
algorithms.
