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5G wireless networks leverage complex scheduling, retransmission, and adaptation mechanisms to maximize their efficiency. These mechanisms interact to produce significant fluctuations in uplink and downlink capacity and latency, markedly impacting the the performance of real-time communication and multimedia applications, such as video conferencing. These applications are particularly sensitive to such fluctuations, resulting in lag, stuttering, distorted audio, and low video quality. In this paper, we present a cross-layer view of 5G networks and their impact on and interaction with video-conferencing applications. We conduct novel, detailed measurements of both private CBRS and commercial carrier cellular network dynamics, capturing physical- and link-layer events and correlating them with their effects at the network and transport layers, and the video-conferencing application itself. Our two datasets comprise days of low-rate campus-wide Zoom telemetry data, and hours of high-rate, correlated WebRTC-network-5G telemetry data. Based on these data, we trace performance anomalies back to root causes, identifying 24 previously unknown causal event chains that degrade 5G video conferencing. Armed with this knowledge, we build Domino, a tool that automates this process and is user-extensible to future wireless networks and interactive applications. 

Dataset release for the paper "Automated, Cross-Layer Root Cause Analysis of 5G Video-Conferencing Quality Degradation"; published in the proceedings of ACM IMC 2025. 

Release for IMC (v0.1). This repository provides the experimental setup and automation scripts used for measuring WebRTC performance over 5G networks. It reproduces the setup used in our paper, where: One WebRTC client runs on a Google Cloud VM, and the other client runs on a local machine connected via T-Mobile 5G. The setup includes video streaming via virtual cameras, synchronized system clocks, and data collection through tcpdump and custom packet parsers. 

Rapid delay variations in today’s access networks impair the QoE of low-latency, interactive applications, such as video conferencing. To tackle this problem, we propose Athena, a framework that correlates high-resolution measurements from Layer 1 to Layer 7 to remove the fog from the window through which today’s video-conferencing congestion-control algorithms see the network. This cross-layer view of the network empowers the networking community to revisit and re-evaluate their network designs and application scheduling and rate-adaptation algorithms in light of the complex, heterogeneous networks that are in use today, paving the way for network-aware applications and application-aware networks.