Paper Info
Title
Predictive Adaptive Streaming to Enable Mobile 360-Degree and VR Experiences
Abstract
As 360-degree videos and virtual reality (VR) applications become popular for consumer and enterprise use cases, the desire to enable truly mobile experiences also increases. Delivering 360-degree videos and cloud/edge-based VR applications require ultra-high bandwidth and ultra-low latency <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , challenging to achieve with mobile networks. A common approach to reduce bandwidth is streaming only the field of view (FOV). However, extracting and transmitting the FOV in response to user head motion can add high latency, adversely affecting user experience. In this paper, we propose a predictive adaptive streaming approach, where the predicted view with high predictive probability is adaptively encoded in relatively high quality according to bandwidth conditions and transmitted in advance, leading to a simultaneous reduction in bandwidth and latency. The predictive adaptive streaming method is based on a deep-learning-based viewpoint prediction model we develop, which uses past head motions to predict where a user will be looking in the 360-degree view. Using a very large dataset consisting of head motion traces from over 36,000 viewers for nineteen 360-degree/VR videos, we validate the ability of our predictive adaptive streaming method to offer high-quality view while simultaneously significantly reducing bandwidth.
Year
DOI
Venue
2021
10.1109/TMM.2020.2987693
IEEE Transactions on Multimedia
Keywords
DocType
Volume
Virtual reality,video streaming,360-degree video
Journal
23
ISSN
Citations 
PageRank 
1520-9210
4
0.39
References 
Authors
0
4
Name
Order
Citations
PageRank
Xueshi Hou11447.32
Sujit Dey23067278.74
Jianzhong Zhang3155.45
Madhukar Budagavi426723.26