Abstract
In this paper, we perform a first comprehensive study of mobile volumetric video streaming. Volumetric videos are truly 3D, allowing six degrees of freedom (6DoF) movement for their viewers during playback. Such flexibility enables numerous applications in entertainment, healthcare, education, etc. However, volumetric video streaming is extremely bandwidth-intensive. We conduct a detailed investigation of each of the following aspects for point cloud streaming (a popular volumetric data format): encoding, decoding, segmentation, viewport movement patterns, and viewport prediction. Motivated by the observations from the above study, we propose ViVo, which is to the best of our knowledge the first practical mobile volumetric video streaming system with three visibility-aware optimizations. ViVo judiciously determines the video content to fetch based on how, what and where a viewer perceives for reducing bandwidth consumption of volumetric video streaming. Our evaluations over real wireless networks (including commercial 5G), mobile devices and users indicate that ViVo can save on average 40% of data usage (up to 80%) with virtually no drop in visual quality.
| Original language | English (US) |
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| Title of host publication | Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, MobiCom 2020 |
| Publisher | Association for Computing Machinery |
| Pages | 137-149 |
| Number of pages | 13 |
| ISBN (Electronic) | 9781450370851 |
| DOIs | |
| State | Published - Apr 16 2020 |
| Event | 26th Annual International Conference on Mobile Computing and Networking, MobiCom 2020 - London, United Kingdom Duration: Sep 21 2020 → Sep 25 2020 |
Publication series
| Name | Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM |
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Conference
| Conference | 26th Annual International Conference on Mobile Computing and Networking, MobiCom 2020 |
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| Country/Territory | United Kingdom |
| City | London |
| Period | 9/21/20 → 9/25/20 |
Bibliographical note
Funding Information:We appreciate the anonymous reviewers and our shepherd for their valuable comments. We thank Jarrell Pair for capturing the volumetric videos and Cheuk Yiu Ip for insightful discussions. We thank the voluntary users who participated in our user study, as well as Arvind Narayanan for helping with the 5G experiments. Feng Qian’s research was supported in part by NSF Award #1903880 and #1915122.
Publisher Copyright:
© 2020 ACM.