  In the tile-based 360-degree video streaming, predicting user's future
viewpoints and developing adaptive bitrate (ABR) algorithms are essential for
optimizing user's quality of experience (QoE). Traditional single-user based
viewpoint prediction methods fail to achieve good performance in long-term
prediction, and the recently proposed reinforcement learning (RL) based ABR
schemes applied in traditional video streaming can not be directly applied in
the tile-based 360-degree video streaming due to the exponential action space.
Therefore, we propose a sequential reinforced 360-degree video streaming scheme
with cross-user attentive network. Firstly, considering different users may
have the similar viewing preference on the same video, we propose a cross-user
attentive network (CUAN), boosting the performance of long-term viewpoint
prediction by selectively utilizing cross-user information. Secondly, we
propose a sequential RL-based (360SRL) ABR approach, transforming action space
size of each decision step from exponential to linear via introducing a
sequential decision structure. We evaluate the proposed CUAN and 360SRL using
trace-driven experiments and experimental results demonstrate that CUAN and
360SRL outperform existing viewpoint prediction and ABR approaches with a
noticeable margin.
