Poster: Optimizing Mobile Video Telephony Using Deep Imitation Learning

Abstract

Despite the pervasive use of real-time video telephony services, their quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. We conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. We train \name with the massive data traces from the measurement campaign using a custom-designed imitation learning algorithm, which enables \name to learn from past experience following an expert's iterative demonstration/supervision. We have implemented and incorporated \name into the \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.