Emerging mobile virtual reality (VR) systems will require to continuously perform complex computer vision tasks on ultra-high-resolution video frames through the execution of deep neural networks (DNNs)-based algorithms. Since state-of-the-art DNNs require computational power that is excessive for mobile devices, techniques based on wireless edge computing (WEC) have been recently proposed. However, existing WEC methods require the transmission and processing of a high amount of video data which may ultimately saturate the wireless link. In this paper, we propose a novel Sensing-Assisted Wireless Edge Computing (SAWEC) paradigm to address this issue. SAWEC leverages knowledge about the physical environment to reduce the end-to-end latency and overall computational burden by transmitting to the edge server only the relevant data for the delivery of the service. Our intuition is that the transmission of the portion of the video frames where there are no changes with respect to previous frames can be avoided. Specifically, we leverage wireless sensing techniques to estimate the location of objects in the environment and obtain insights about the environment dynamics. Hence, only the part of the frames where any environmental change is detected is transmitted and processed. We evaluated SAWEC by using a 10K 360$^{\circ}$ camera with a Wi-Fi 6 sensing system operating at 160 MHz and performing localization and tracking. We perform experiments in an anechoic chamber and a hall room with two human subjects in six different setups. Experimental results show that SAWEC reduces the channel occupation, and end-to-end latency by 93.81%, and 96.19% respectively while improving the instance segmentation performance by 46.98% with respect to state-of-the-art WEC approaches. For reproducibility purposes, we pledge to share our whole dataset and code repository.

翻译：新兴的移动虚拟现实（VR）系统需要通过对超高分辨率视频帧执行基于深度神经网络（DNN）的算法，持续完成复杂的计算机视觉任务。由于现有最先进的DNN对移动设备而言计算能力需求过高，近年来提出了基于无线边缘计算（WEC）的技术方案。然而，现有WEC方法需要传输和处理大量视频数据，这最终可能导致无线链路饱和。本文提出了一种新颖的传感辅助无线边缘计算（SAWEC）范式以解决该问题。SAWEC利用对物理环境的认知，仅向边缘服务器传输提供服务所需的相关数据，从而降低端到端延迟和整体计算负担。我们的直觉是，可避免传输视频帧中与前一帧相比无变化的部分。具体而言，我们利用无线传感技术估计环境中物体的位置，并获取环境动态信息。因此，仅传输和处理检测到环境变化的帧部分。我们通过使用10K 360°摄像头与工作在160 MHz频段的Wi-Fi 6传感系统（执行定位与追踪）对SAWEC进行了评估。在消声室和礼堂中，针对两名受试者设置了六种不同场景开展实验。实验结果表明，与现有最先进的WEC方法相比，SAWEC将信道占用率降低93.81%，端到端延迟降低96.19%，同时实例分割性能提升46.98%。为推动研究可复现，我们承诺共享全部数据集与代码仓库。