2 research outputs found
Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos
The 360-degree video allows users to enjoy the whole scene by interactively
switching viewports. However, the huge data volume of the 360-degree video
limits its remote applications via network. To provide high quality of
experience (QoE) for remote web users, this paper presents a tile-based
adaptive streaming method for 360-degree videos. First, we propose a simple yet
effective rate adaptation algorithm to determine the requested bitrate for
downloading the current video segment by considering the balance between the
buffer length and video quality. Then, we propose to use a Gaussian model to
predict the field of view at the beginning of each requested video segment. To
deal with the circumstance that the view angle is switched during the display
of a video segment, we propose to download all the tiles in the 360-degree
video with different priorities based on a Zipf model. Finally, in order to
allocate bitrates for all the tiles, a two-stage optimization algorithm is
proposed to preserve the quality of tiles in FoV and guarantee the spatial and
temporal smoothness. Experimental results demonstrate the effectiveness and
advantage of the proposed method compared with the state-of-the-art methods.
That is, our method preserves both the quality and the smoothness of tiles in
FoV, thus providing the best QoE for users.Comment: 16 pages, This paper has been accepted by the IEEE Journal of
Selected Topics in Signal Processin
Online Bitrate Selection for Viewport Adaptive 360-Degree Video Streaming
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