  The popularity of immersive videos has prompted extensive research into
neural adaptive tile-based streaming to optimize video transmission over
networks with limited bandwidth. However, the diversity of users' viewing
patterns and Quality of Experience (QoE) preferences has not been fully
addressed yet by existing neural adaptive approaches for viewport prediction
and bitrate selection. Their performance can significantly deteriorate when
users' actual viewing patterns and QoE preferences differ considerably from
those observed during the training phase, resulting in poor generalization. In
this paper, we propose MANSY, a novel streaming system that embraces user
diversity to improve generalization. Specifically, to accommodate users'
diverse viewing patterns, we design a Transformer-based viewport prediction
model with an efficient multi-viewport trajectory input output architecture
based on implicit ensemble learning. Besides, we for the first time combine the
advanced representation learning and deep reinforcement learning to train the
bitrate selection model to maximize diverse QoE objectives, enabling the model
to generalize across users with diverse preferences. Extensive experiments
demonstrate that MANSY outperforms state-of-the-art approaches in viewport
prediction accuracy and QoE improvement on both trained and unseen viewing
patterns and QoE preferences, achieving better generalization.
