Power-efficient live virtual reality streaming using edge offloading
Power-efficient live virtual reality streaming using edge offloading
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DOI：
10.1145/3534088.3534351
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Ziehen Zhu;Xianglong Feng;Zhongze Tang;Nan Jiang;Tian Guo;Lisong Xu;Sheng Wei
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文献类型：
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作者：
Ziehen Zhu;Xianglong Feng;Zhongze Tang;Nan Jiang;Tian Guo;Lisong Xu;Sheng Wei
本文旨在解决实时虚拟现实（VR）流媒体（也称为360度视频流媒体）中的重大功耗挑战，其中VR视图渲染和高级深度学习操作（例如，超分辨率）消耗了相当大的电量，耗尽了电池有限的VR头显。我们开发了EdgeVR，这是一种用于实时VR流媒体的电源优化技术，可以将设备上的VR渲染和深度学习操作卸载到边缘服务器上，从而节省电力。为了解决由于边缘卸载导致的显著增加的运动到光子（MtoP）延迟，我们开发了一种实时VR视口预测方法，以在边缘服务器上预渲染VR视图并补偿往返延迟。我们使用端到端实时VR流媒体系统评估EdgeVR的有效性，该系统包含48个用户观看9个VR视频的经验VR头部运动数据集。结果表明，EdgeVR实现了低MtoP延迟的节能实时VR流。
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.