  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%.
