Asynchronous DRL-based Bitrate Selection for 360- Degree Video Streaming over THz Wireless Systems

360° videos demand substantial bandwidth to deliver an immersive viewing experience to users. In wireless networks, this high data rate demand can be accommodated by utilizing the terahertz (THz) frequency band. However, THz band communications are susceptible to self-blockage. To ensure reliable transmission, this paper studies the streaming of
$360^{\circ}$
videos over THz wireless systems using multiple multi-antenna access points (APs). Users' requests for video tiles give rise to an optimization problem that involves asynchronous bitrate selection for those tiles and beamforming design for the APs. We formulate this problem as a macro-action decentralized partially observable Markov decision process (MacDec-POMDP). To efficiently tackle this problem for multiple users, we propose an asynchronous deep reinforcement learning (DRL) algorithm using a multi-agent actor-critic method to determine the bitrate selection policy. The APs' beamforming is determined by solving an optimization problem using the weighted minimum mean square error (WMMSE) algorithm. Results show that our proposed approach provides a higher average quality of experience (QoE) for the users when compared with two benchmark algorithms.