DAO: Dynamic Adaptive Offloading for Video Analytics
DAO: Dynamic Adaptive Offloading for Video Analytics
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DOI：
10.1145/3503161.3548249
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Taslim Murad;Anh Nguyen;Zhisheng Yan
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文献类型：
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作者：
Taslim Murad;Anh Nguyen;Zhisheng Yan
将视频从终端设备转移到边缘或云服务器是实现计算密集型视频分析的关键。为确保服务器分析的准确性，必须根据特定内容和可用网络带宽配置用于卸载的视频质量。虽然针对用户观看的自适应视频流已经得到了广泛的研究，但现有的工作都不能保证在服务器端以带宽和内容自适应的方式进行分析的准确性。为了填补这一空白，本文提出了DAO，这是一种动态自适应的视频分析框架，它联合考虑了网络带宽和视频内容的动态变化。DAO能够通过动态调整视频比特率和分辨率来最大限度地提高服务器的分析精度。从本质上讲，我们将自适应视频传输的环境从传统的DASH系统转移到了为视频分析量身定做的新的动态自适应卸载框架。DAO得到了有关分析准确性、视频内容、比特率和分辨率之间内在关系的一些新发现的支持，以及动态适应比特率和分辨率的优化公式。实际目标检测任务的实现结果表明，DAO的性能接近理论极限，与传统的DASH方案相比，带宽节省了20%，类别MAP改善了59%。
Offloading videos from end devices to edge or cloud servers is the key to enabling computation-intensive video analytics. To ensure the analytics accuracy at the server, the video quality for offloading must be configured based on the specific content and the available network bandwidth. While adaptive video streaming for user viewing has been widely studied, none of the existing works can guarantee the analytics accuracy at the server in bandwidth- and content-adaptive way. To fill in this gap, this paper presents DAO, a dynamic adaptive offloading framework for video analytics that jointly considers the dynamics of network bandwidth and video content. DAO is able to maximize the analytics accuracy at the server by adapting the video bitrate and resolution dynamically. In essence, we shift the context of adaptive video transport from traditional DASH systems to a new dynamic adaptive offloading framework tailored for video analytics. DAO is empowered by some new discoveries about the inherent relationship between analytics accuracy, video content, bitrate, and resolution, as well as by an optimization formulation to adapt the bitrate and resolution dynamically. Results from the real-world implementation of object detection tasks show that DAO's performance is close to the theoretical bound, achieving 20% bandwidth saving and 59% category-wise mAP improvement compared to conventional DASH schemes.