Better Never Than Late: Timely Edge Video Analytics Over the Air

ABSTRACTEdge video analytics based on deep learning has become an important building block for many modern intelligent applications such as mobile augmented reality and autonomous driving. Various mechanisms have been developed to handle dynamic wireless networks, compute resource availability, and achieve high analytics accuracy via filtering, DNN compression, pruning, and adaptation. So far, limited attention has been paid to timeliness---providing strict service-level objectives (SLO) for edge video analytics pipelines, which is essential for the usability of user-interactive and mission-critical intelligent applications. In this paper, we analyze the challenges in achieving SLO for edge video analytics and present a system design for timely edge video analytics over the air leveraging a simple yet effective idea---feedback control. Our preliminary evaluation based on a system prototype and real-world network traces shows the potential of our design. We also discuss the limitations, calling for future work.