  A key challenge of 360$^\circ$ VR video streaming is ensuring high quality
with limited network bandwidth. Currently, most studies focus on tile-based
adaptive bitrate streaming to reduce bandwidth consumption, where resources in
network nodes are not fully utilized. This article proposes a tile-weighted
rate-distortion (TWRD) packet scheduling optimization system to reduce data
volume and improve video quality. A multimodal spatial-temporal attention
transformer is proposed to predict viewpoint with probability that is used to
dynamically weight tiles and corresponding packets. The packet scheduling
problem of determining which packets should be dropped is formulated as an
optimization problem solved by a dynamic programming solution. Experiment
results demonstrate the proposed method outperforms the existing methods under
various conditions.
