YuZu: Neural-Enhanced Volumetric Video Streaming


Anlan Zhang and Chendong Wang, University of Minnesota, Twin Cities; Bo Han, George Mason University; Feng Qian, University of Minnesota, Twin Cities


Differing from traditional 2D videos, volumetric videos provide true 3D immersive viewing experiences and allow viewers to exercise six degree-of-freedom (6DoF) motion. However, streaming high-quality volumetric videos over the Internet is extremely bandwidth-consuming. In this paper, we propose to leverage 3D super resolution (SR) to drastically increase the visual quality of volumetric video streaming. To accomplish this goal, we conduct deep intra- and inter-frame optimizations for off-the-shelf 3D SR models, and achieve up to 542× speedup on SR inference without accuracy degradation. We also derive a first Quality of Experience (QoE) model for SR-enhanced volumetric video streaming, and validate it through extensive user studies involving 1,446 subjects, achieving a median QoE estimation error of 12.49%. We then integrate the above components, together with important features such as QoE-driven network/compute resource adaptation, into a holistic system called YuZu that performs line-rate (at 30+ FPS) adaptive SR for volumetric video streaming. Our evaluations show that YuZu can boost the QoE of volumetric video streaming by 37% to 178% compared to no SR, and outperform existing viewport-adaptive solutions by 101% to 175% on QoE.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {278306,
author = {Anlan Zhang and Chendong Wang and Bo Han and Feng Qian},
title = {{YuZu}: {Neural-Enhanced} Volumetric Video Streaming},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {137--154},
url = {https://www.usenix.org/conference/nsdi22/presentation/zhang-anlan},
publisher = {USENIX Association},
month = apr

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