  360-degree video streaming provides users with immersive experience by
letting users determine their field-of-views (FoVs) in real time. To enhance
the users' quality of experience (QoE) given their limited bandwidth, recent
works have proposed a viewport adaptive 360-degree video streaming model by
exploiting the bitrate adaptation in spatial and temporal domains. Under this
video streaming model, in this paper, we consider a scenario with a newly
generated 360-degree video without viewing history from other users. To
maximize the user's QoE, we propose an online bitrate selection algorithm,
called OBS360. The proposed online algorithm can adapt to the unknown and
heterogeneous users' FoVs and downloading capacities. We prove that the
proposed algorithm achieves sublinear dynamic regret under a convex decision
set. This suggests that as the number of video segments increases, the
performance of the online algorithm approaches the performance of the offline
algorithm, where the users' FoVs and downloading capacities are known. We
perform simulations with real-world dataset to evaluate the performance of the
proposed algorithm. Results show that compared with several existing methods,
our proposed algorithm can enhance the users' QoE significantly by improving
the viewing bitrate and reducing the inter-segment and intra-segment
degradation losses of the users.
