Inferring the quality of network services is the vital basis of optimization for network operators. However, prevailing real-time video streaming applications adopt encryption for security, leaving it a problem to extract Quality of Service (QoS) indicators of real-time video. In this paper, we propose DaI, a traffic-based real-time video quality estimator. DaI can partially decrypt the encrypted real-time video data and applies machine learning methods to estimate key objective Quality of Experience (QoE) metrics of real-time video. According to the experimental results, DaI can estimate objective QoE metrics with an average accuracy of 79%.
翻译:计算网络服务质量是网络运营商优化的关键基础,然而,目前流行的实时视频流应用采用安全加密,因此难以提取实时视频服务质量指标(QoS),在本文中,我们提出DaI,一个基于交通的实时视频质量测量器,DaI可以部分解密加密实时视频数据,并运用机器学习方法估计实时视频的关键客观质量标准。根据实验结果,DaI可以平均精确度为79%对目标QoE指标进行估算。