A Lyapunov Optimization-Based Online Algorithm for Scheduling Cloud-Edge Collaborative Real-Time Video Stream Analytics Tasks

With the rapid development of cities and the increase in the number of motor vehicles, traditional intelligent traffic video analytics systems face a significant challenge due to soaring computational demands and limited network transmission resources. Today’s widely used cloud-based traffic video analytics system, which is generally reliant on transmitting all video data to centralized cloud servers, suffer from high latency during network fluctuations and inability to respond promptly to urban traffic management. This article introduces a cloud-edge collaborative framework for traffic video analytics. Within this framework, edge servers serve as intermediaries between video sources and the cloud center. The framework prioritizes the offloading of computing tasks to nodes located near the video sources, rationally leveraging the limited computing and network resources to mitigate network transmission load. We develop a measurement-based analytical model to describe the tradeoffs among network latency, inference latency, and analytics accuracy in edge-based real-time video analytics system. As a core element of our approach, we present a resolution selection and bandwidth allocation algorithm based on Lyapunov optimization and heuristic search, designed to dynamically adjust video resolution and distribute bandwidth between edge and cloud servers to balance latency and accuracy without requiring future information. Experiments on a cloud-edge collaborative video analytics system demonstrate the algorithm’s effectiveness in substantially enhancing accuracy and responsiveness.