@inproceedings{e2259b6601d5435090d2d28f7f44dc31,
title = "Power-efficient live virtual reality streaming using edge offloading",
abstract = "This paper aims to address the significant power challenges in live virtual reality (VR) streaming (a.k.a., 360-degree video streaming), where the VR view rendering and the advanced deep learning operations (e.g., super-resolution) consume a considerable amount of power draining the battery-constrained VR headset. We develop EdgeVR, a power optimization technique for live VR streaming, which offloads the on-device VR rendering and deep learning operations to an edge server for power savings. To address the significantly increased motion-to-photon (MtoP) latency due to the edge offloading, we develop a live VR viewport prediction method to pre-render the VR views on the edge server and compensate for the round-trip delays. We evaluate the effectiveness of EdgeVR using an end-to-end live VR streaming system with an empirical VR head movement dataset involving 48 users watching 9 VR videos. The results reveal that EdgeVR achieves power-efficient live VR streaming with low MtoP latency.",
keywords = "Live streaming, power efficiency, virtual reality",
author = "Ziehen Zhu and Xianglong Feng and Zhongze Tang and Nan Jiang and Tian Guo and Lisong Xu and Sheng Wei",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 32nd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2022 ; Conference date: 17-06-2022",
year = "2022",
month = jun,
day = "17",
doi = "10.1145/3534088.3534351",
language = "English (US)",
series = "NOSSDAV 2022 - Proceedings of the 2022 Workshop on Network and Operating System Support for Digital Audio and Video, Part of MMSys 2022",
publisher = "Association for Computing Machinery, Inc",
pages = "57--63",
booktitle = "NOSSDAV 2022 - Proceedings of the 2022 Workshop on Network and Operating System Support for Digital Audio and Video, Part of MMSys 2022",
}