Presenter: Gianluca Perna
Monday, January 24th, 2022 17:30
Real-time communication (RTC) applications have become largely popular in the last decade with the spread of broadband and mobile Internet access. Nowadays, these platforms are a fundamental means for connecting people and supporting businesses that increasingly rely on forms of remote work. In this context, it is of paramount importance to operate at the network level to ensure adequate Quality of Experience (QoE) for users, and appropriate traffic management policies are essential to prioritize RTC traffic. This in turn requires the network to be able to identify RTC streams and the type of content they carry.
We propose a machine learning-based application to classify media streams generated by RTC applications encapsulated in Secure Real-Time Protocol (SRTP) flows in real-time. Using carefully tuned features extracted from packet characteristics, we train models to classify streams into a variety of classes, including media type (audio/video), video quality, and redundant streams. We validate our approach using traffic from over 62 hours of multi-party meetings conducted using two popular RTC applications, namely Cisco Webex Teams and Jitsi Meet.
Biography: A PhD student in Electrical, Electronics and Communications Engineering in collaboration with the SmartData Research group at Politecnico di Torino. Completed his Bachelor studies in Telecommunication engineering and Master studies in ICT for Smart Societies. His research is focused on machine learning techniques for Quality of Experience (QoE) estimation, used for traffic network management.