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With more videos being recorded by edge sensors (cameras) and analyzed bycomputer-vision deep neural nets (DNNs), a new breed of video streaming systemshas emerged, with the goal to compress and stream videos to remote servers inreal time while preserving enough information to allow highly accurateinference by the server-side DNNs. An ideal design of the video streamingsystem should simultaneously meet three key requirements: (1) low latency ofencoding and streaming, (2) high accuracy of server-side DNNs, and (3) lowcompute overheads on the camera. Unfortunately, despite many recent efforts,such video streaming system has hitherto been elusive, especially when servingadvanced vision tasks such as object detection or semantic segmentation. Thispaper presents AccMPEG, a new video encoding and streaming system that meetsall the three requirements. The key is to learn how much the encoding qualityat each (16x16) macroblock can influence the server-side DNN accuracy, which wecall accuracy gradient. Our insight is that these macroblock-level accuracygradient can be inferred with sufficient precision by feeding the video framesthrough a cheap model. AccMPEG provides a suite of techniques that, given a newserver-side DNN, can quickly create a cheap model to infer the accuracygradient on any new frame in near realtime. Our extensive evaluation of AccMPEGon two types of edge devices (one Intel Xeon Silver 4100 CPU or NVIDIA JetsonNano) and three vision tasks (six recent pre-trained DNNs) shows that AccMPEG(with the same camera-side compute resources) can reduce the end-to-endinference delay by 10-43% without hurting accuracy compared to thestate-of-the-art baselines

AccMPEG: Optimizing Video Encoding for Video Analytics