Abstract
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.
| Original language | English (US) |
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| Title of host publication | Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2022 |
| Publisher | USENIX Association |
| Pages | 137-154 |
| Number of pages | 18 |
| ISBN (Electronic) | 9781939133274 |
| State | Published - 2022 |
| Event | 19th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2022 - Renton, United States Duration: Apr 4 2022 → Apr 6 2022 |
Publication series
| Name | Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2022 |
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Conference
| Conference | 19th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2022 |
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| Country/Territory | United States |
| City | Renton |
| Period | 4/4/22 → 4/6/22 |
Bibliographical note
Funding Information:We thank the anonymous reviewers and our shepherd Anirudh Badam for their insightful comments. The research of Feng Qian was supported in part by a Cisco research award. The research of Bo Han was funded in part by 4-VA, a collaborative partnership for advancing the Commonwealth of Virginia.
Publisher Copyright:
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