Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck (PIB) framework for edge video analytics. This framework prioritizes the shared data based on the signal-to-noise ratio (SNR) and camera coverage of the region of interest (RoI), reducing spatial-temporal data redundancy to transmit only essential information. This strategy avoids the need for video reconstruction at edge servers and maintains low latency. It leverages a deterministic information bottleneck method to extract compact, relevant features, balancing informativeness and communication costs. For high-dimensional data, we apply variational approximations for practical optimization. To reduce communication costs in fluctuating connections, we propose a gate mechanism based on distributed online learning (DOL) to filter out less informative messages and efficiently select edge servers. Moreover, we establish the asymptotic optimality of DOL by proving the sublinearity of their regrets. To validate the effectiveness of the PIB framework, we conduct real-world experiments on three types of edge devices with varied computing capabilities. Compared to five coding methods for image and video compression, PIB improves mean object detection accuracy (MODA) while reducing 17.8% and reduces communication costs by 82.65% under poor channel conditions.

翻译：协同感知系统利用多个边缘设备（如监控摄像头或自动驾驶汽车）来提升感知质量并消除盲区。尽管具备优势，但有限的信道容量和数据冗余等挑战阻碍了其效能。为解决这些问题，我们提出了面向边缘视频分析的优先信息瓶颈框架。该框架基于感兴趣区域的信噪比与摄像头覆盖范围对共享数据进行优先级排序，通过减少时空数据冗余来仅传输关键信息。此策略避免了在边缘服务器端进行视频重建的需求，并保持了低延迟。它利用确定性信息瓶颈方法提取紧凑且相关的特征，以平衡信息量与通信成本。针对高维数据，我们采用变分近似方法进行实际优化。为降低波动连接中的通信开销，我们提出了一种基于分布式在线学习的门控机制，以过滤信息量较低的消息并高效选择边缘服务器。此外，我们通过证明其遗憾值的次线性特性，确立了分布式在线学习的渐近最优性。为验证优先信息瓶颈框架的有效性，我们在三种不同计算能力的边缘设备上进行了真实场景实验。与五种图像及视频压缩编码方法相比，优先信息瓶颈在恶劣信道条件下将平均目标检测精度提升了17.8%，同时降低了82.65%的通信成本。