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Video Conferencing Applications (VCAs) that support remote work and education have increased in use over the last two years, contributing to Internet bandwidth usage. VCA clients transmit video and audio to each other in peer-to-peer mode or through a bridge known as a Selective Forwarding Unit (SFU). Popular VCAs implement congestion control in the application layer over UDP and accomplish rate adjustment through video rate control, ultimately affecting end user Quality of Experience(QoE). Researchers have reported on the throughput and video metric performance of specific VCAs using structuredexperiments. Yet prior work rarely examines the interaction between congestion control mechanisms and rate adjustment techniques that produces the observed throughput and QoE metrics. Understanding this interaction at a functional level paves the way to explain observed performance, to pinpoint commonalities and key functional differences across VCAs, and to contemplate opportunities for innovation. To that end, we first design and conduct detailed measurements of three VCAs(WebRTC/Jitsi, Zoom, Blue Jeans) to develop understanding of their congestion and video rate control mechanisms. We then use the measurement results to derive our functional models for the VCA client and SFU. Our models reveal the complexity of these systems and demonstrate how, despite some uniformity in function deployment, there is significant variability among the VCAs in the implementation of these functions. 

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs for maintaining an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about the device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability. 

Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask: "Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.