The vast adoption of mobile devices with cameras has greatly contributed to the proliferation of the creation and distribution of videos. For a variety of purposes, valuable information may be extracted from these videos. While the computational capability of mobile devices has greatly improved recently, video processing is still a demanding task for mobile devices. We design an on-demand video processing system, NetVision , that performs distributed video processing using deep learning across a wireless network of mobile and edge devices to answer queries while minimizing the query response time. However, the problem of minimal query response time for processing videos stored across a network is a strongly NP-hard problem. To deal with this, we design a greedy algorithm with bounded performance. To further deal with the dynamics of the transmission rate between mobile and edge devices, we design an adaptive algorithm. We built NetVision and deployed it on a small testbed. Based on the measurements of the testbed and by extensive simulations, we show that the greedy algorithm is close to the optimum and the adaptive algorithm performs better with more dynamic transmission rates. We then perform experiments on the small testbed to examine the realized system performance in both stationary networks and mobile networks.

中文翻译：

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NetVision：无线网络中的点播视频处理

带摄像头的移动设备的广泛采用极大地促进了视频创作和发行的增长。为了各种目的，可以从这些视频中提取有价值的信息。尽管移动设备的计算能力最近得到了极大的提高，但是视频处理仍然是移动设备的一项艰巨任务。我们设计了一个点播视频处理系统，网视 ，它使用跨移动和边缘设备的无线网络的深度学习执行分布式视频处理，以在最小化查询响应时间的同时回答查询。但是，用于处理跨网络存储的视频的查询响应时间最短的问题是一个非常棘手的NP问题。为了解决这个问题，我们设计了性能有限的贪婪算法。为了进一步处理移动设备和边缘设备之间传输速率的动态变化，我们设计了一种自适应算法。我们构建了NetVision，并将其部署在一个小型测试平台上。基于测试平台的测量结果和广泛的仿真结果，我们表明，贪婪算法接近于最优算法，自适应算法在动态传输速率更高的情况下表现更好。