To address the conflict between the limited network bandwidth and high DNN inference accuracy, live video analytics desires a bandwidth-efficient streaming approach. To this end, more and more works study spatially variable quality streaming where high quality is only used for important regions. The key challenges are to accurately identify the important regions and select the right qualities for them to maximize accuracy. Existing approaches use either cheap analytics models or low-quality videos to locate important regions, and employ heuristic rules to make quality decisions, which struggle to address the above challenges. Our key insight is that the region’s accuracy “sensitivity” obtained by running the expensive DNN model on the high-quality video provides a reliable indication of the region’s importance and allows to allocate the available bandwidth optimally over regions by explicitly maximizing the frame accuracy. This work presents a sensitivity-aware algorithm Orchestra, which incorporates sensitivity into the design of spatial quality adaptation, including video zoning and quality selection. The design of Orchestra entails three main contributions: a feasible way of sensitivity estimation, sensitivity-aware zoning, and deduction-based accuracy estimation. Extensive experiments over realistic videos and network traces show that Orchestra improves accuracy by upto 14.1% with comparable bandwidth usage or reduces bandwidth usage by upto 44.2% while maintaining higher accuracy compared to baselines.

中文翻译：

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实时视频分析的灵敏度感知空间质量适应

为了解决有限的网络带宽和高 DNN 推理精度之间的冲突，实时视频分析需要一种带宽高效的流媒体方法。为此，越来越多的作品研究空间可变质量流，其中高质量仅用于重要区域。关键挑战是准确识别重要区域并为它们选择正确的质量以最大限度地提高准确性。现有方法使用廉价的分析模型或低质量的视频来定位重要区域，并采用启发式规则来做出质量决策，这些都难以应对上述挑战。我们的关键见解是，通过在高质量视频上运行昂贵的 DNN 模型获得的区域准确度“敏感性”提供了该区域重要性的可靠指示，并允许通过显式最大化帧准确度在区域上优化分配可用带宽。这项工作提出了一种敏感度感知算法 Orchestra，它将敏感度纳入空间质量适应的设计，包括视频分区和质量选择。Orchestra 的设计需要三个主要贡献：敏感性估计的可行方法、敏感性感知分区和基于演绎的准确度估计。对真实视频和网络跟踪的广泛实验表明，Orchestra 在带宽使用量相当的情况下将准确度提高了 14.1%，或将带宽使用量减少了多达 44。