  Over the last few years, 360{\deg} video traffic on the network has grown
significantly. A key challenge of 360{\deg} video playback is ensuring a high
quality of experience (QoE) with limited network bandwidth. Currently, most
studies focus on tile-based adaptive bitrate (ABR) streaming based on single
viewport prediction to reduce bandwidth consumption. However, the performance
of models for single-viewpoint prediction is severely limited by the inherent
uncertainty in head movement, which can not cope with the sudden movement of
users very well. This paper first presents a multimodal spatial-temporal
attention transformer to generate multiple viewpoint trajectories with their
probabilities given a historical trajectory. The proposed method models
viewpoint prediction as a classification problem and uses attention mechanisms
to capture the spatial and temporal characteristics of input video frames and
viewpoint trajectories for multi-viewpoint prediction. After that, a
multi-agent deep reinforcement learning (MADRL)-based ABR algorithm utilizing
multi-viewpoint prediction for 360{\deg} video streaming is proposed for
maximizing different QoE objectives under various network conditions. We
formulate the ABR problem as a decentralized partially observable Markov
decision process (Dec-POMDP) problem and present a MAPPO algorithm based on
centralized training and decentralized execution (CTDE) framework to solve the
problem. The experimental results show that our proposed method improves the
defined QoE metric by up to 85.5% compared to existing ABR methods.
