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360-degree videos require significant bandwidth to provide an immersiveviewing experience. Wireless systems using terahertz (THz) frequency band canmeet this high data rate demand. However, self-blockage is a challenge in suchsystems. To ensure reliable transmission, this paper explores THz-enabled360-degree video streaming through multiple multi-antenna access points (APs).Guaranteeing users' quality of experience (QoE) requires accurate viewportprediction to determine which video tiles to send, followed by asynchronousbitrate selection for those tiles and beamforming design at the APs. To addressusers' privacy and data heterogeneity, we propose a content-based viewportprediction framework, wherein users' head movement prediction models aretrained using a personalized federated learning algorithm. To addressasynchronous decision-making for tile bitrates and dynamic THz linkconnections, we formulate the optimization of bitrate selection and beamformingas 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 onmulti-agent actor-critic methods and propose a hierarchical learning frameworkto train the actor and critic networks. Experimental results show that ourproposed approach provides a higher QoE when compared with three benchmarkalgorithms.

Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360-Degree Video Streaming