Power-efficient live virtual reality streaming using edge offloading

作者：Ziehen Zhu, Xianglong Feng, Zhongze Tang, Nan Jiang, Tian Guo, Lisong Xu, Sheng Wei · 年份：2022 · DOI：10.1145/3534088.3534351 · 被引用次数：9 · 研究领域：Image and Video Quality Assessment、Advanced Image Processing Techniques、Image Enhancement Techniques

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.