To develop smart city and intelligent manufacturing, video cameras are being increasingly deployed. In order to achieve fast and accurate response to live video queries (e.g., license plate recording and object tracking), the real-time high-volume video streams should be delivered and analyzed efficiently. In this article, we introduce an end-edge-cloud coordination framework for low-latency and accurate live video analytics. Considering the locality of video queries, edge platform is designated as the system coordinator. It accepts live video queries and configures the related end cameras to generate video frames that meet quality requirements. By taking into account the latency constraint, edge computing resources are subtly distributed to process the live video frames from different sources such that the analytic accuracy of the accepted video queries can be maximized. Since the amount of required edge computing resource and video quality to accurately address different video queries are unknown in advance, we propose an online video quality and computing resource configuration algorithm to gradually learn the optimal configuration strategy. Extensive simulation results show that as compared to other benchmarks, the proposed configuration algorithm can effectively improve the analytic accuracy, while providing low-latency response.

中文翻译：

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用于低延迟和准确视频分析的边缘协调查询配置


为了发展智慧城市和智能制造，摄像机的部署越来越多。为了实现对实时视频查询（例如车牌记录和对象跟踪）的快速准确响应，应该有效地交付和分析实时大容量视频流。在本文中，我们介绍了一种用于低延迟和准确的实时视频分析的端边云协调框架。考虑到视频查询的局部性，边缘平台被指定为系统协调者。它接受实时视频查询并配置相关终端摄像机以生成满足质量要求的视频帧。通过考虑延迟约束，边缘计算资源被巧妙地分配来处理来自不同来源的实时视频帧，从而可以最大化所接受视频查询的分析准确性。由于准确解决不同视频查询所需的边缘计算资源和视频质量是预先未知的，因此我们提出了一种在线视频质量和计算资源配置算法，以逐步学习最佳配置策略。大量的仿真结果表明，与其他基准测试相比，所提出的配置算法可以有效提高分析精度，同时提供低延迟响应。