  360-degree videos have gained increasing popularity in recent years with the
developments and advances in Virtual Reality (VR) and Augmented Reality (AR)
technologies. In such applications, a user only watches a video scene within a
field of view (FoV) centered in a certain direction. Predicting the future FoV
in a long time horizon (more than seconds ahead) can help save bandwidth
resources in on-demand video streaming while minimizing video freezing in
networks with significant bandwidth variations. In this work, we treat the FoV
prediction as a sequence learning problem, and propose to predict the target
user's future FoV not only based on the user's own past FoV center trajectory
but also other users' future FoV locations. We propose multiple prediction
models based on two different FoV representations: one using FoV center
trajectories and another using equirectangular heatmaps that represent the FoV
center distributions. Extensive evaluations with two public datasets
demonstrate that the proposed models can significantly outperform benchmark
models, and other users' FoVs are very helpful for improving long-term
predictions.
