Real-time video analytics services aim to provide users with accurate recognition results timely. However, existing studies usually fall into the dilemma between reducing delay and improving accuracy. The edge computing scenario imposes strict transmission and computation resource constraints, making balancing these conflicting metrics under dynamic network conditions difficult. In this regard, we introduce the age of processed information (AoPI) concept, which quantifies the time elapsed since the generation of the latest accurately recognized frame. AoPI depicts the integrated impact of recognition accuracy, transmission, and computation efficiency. We derive closed-form expressions for AoPI under preemptive and non-preemptive computation scheduling policies w.r.t. the transmission/computation rate and recognition accuracy of video frames. We then investigate the joint problem of edge server selection, video configuration adaptation, and bandwidth/computation resource allocation to minimize the long-term average AoPI over all cameras. We propose an online method, i.e., Lyapunov-based block coordinate descent (LBCD), to solve the problem, which decouples the original problem into two subproblems to optimize the video configuration/resource allocation and edge server selection strategy separately. We prove that LBCD achieves asymptotically optimal performance. According to the testbed experiments and simulation results, LBCD reduces the average AoPI by up to 10.94X compared to state-of-the-art baselines.

翻译：实时视频分析服务旨在及时为用户提供准确的识别结果。然而，现有研究通常陷入降低延迟与提高准确性之间的两难困境。边缘计算场景施加了严格的传输与计算资源约束，使得在动态网络条件下平衡这些相互冲突的指标变得困难。为此，我们引入了处理信息年龄（AoPI）的概念，该概念量化了自最新被准确识别的帧生成以来所经过的时间。AoPI描述了识别准确性、传输效率与计算效率的综合影响。我们推导了在抢占式与非抢占式计算调度策略下，AoPI关于视频帧的传输/计算速率与识别准确性的闭式表达式。随后，我们研究了边缘服务器选择、视频配置自适应以及带宽/计算资源分配的联合优化问题，以最小化所有摄像头的长期平均AoPI。我们提出了一种在线方法，即基于李雅普诺夫的块坐标下降法（LBCD），以求解该问题。该方法将原问题解耦为两个子问题，分别优化视频配置/资源分配策略与边缘服务器选择策略。我们证明了LBCD能够实现渐近最优性能。根据测试平台实验与仿真结果，与最先进的基线方法相比，LBCD将平均AoPI降低了高达10.94倍。