Over the last few years, 360$\degree$ video traffic on the network has grown
significantly. A key challenge of 360$\degree$ 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$\degree$ 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.

提出了一种多模态时空注意力转换器来生成多个视点轨迹，基于多视点预测的多智能体深度强化学习 (MADRL) 的自适应比特率流媒体算法，用于在不同的网络条件下最大化不同的体验质量 (QoE) 目标，实验结果显示与现有的自适应比特率方法相比，提出的方法可将定义的 QoE 度量改善高达 85.5%。