eMIMIC: Estimating HTTP-Based Video QoE Metrics from Encrypted Network Traffic

Abstract

Understanding the user-perceived Quality of Experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based Adaptive Streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model a HAS session and estimate video QoE metrics such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth of two popular video streaming services collected using a lab testbed. eMIMIC estimates re-buffering ratio within 1 percentage point of ground truth for up to 70% sessions and average bitrate with error under 100 kbps for up to 80% sessions. We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics.