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$^{\circ}$ with a Wi-Fi 6 sensing system operating at 160 MHz and performing localization and tracking. We considered instance segmentation and object detection as benchmarking tasks for performance evaluation. We carried out experiments in an anechoic chamber and an entrance hall with two human subjects in six different setups. Experimental results show that SAWEC reduces both the channel occupation and end-to-end latency by more than 90% while improving the instance segmentation and object detection performance with respect to state-of-the-art WEC approaches.

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。由于最先进的DNN所需的计算能力远超移动设备的承载范围，基于无线边缘计算（WEC）的技术近年来被提出。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感知辅助无线边缘计算（SAWEC）范式来解决此问题。SAWEC利用对物理环境的认知，仅向边缘服务器传输与提供服务相关的数据，从而降低端到端延迟和整体计算负担。我们的直觉是，可以避免传输视频帧中与前一帧无变化的部分。具体而言，我们利用无线感知技术来估计环境中物体的位置，并获得关于环境动态的洞察。因此，仅传输和处理帧中检测到环境变化的部分。我们通过使用工作在160 MHz的Wi-Fi 6感知系统进行定位与跟踪，并在10K 360$^{\circ}$视频场景下评估了SAWEC。我们选取实例分割与目标检测作为基准任务进行性能评估。我们在消音室和门厅中，针对六种不同设置，让两名人类受试者参与实验。实验结果表明，相较于最先进的WEC方法，SAWEC在降低超过90%的信道占用率和端到端延迟的同时，提升了实例分割与目标检测的性能。