# Measuring the Performance and Network Utilization of Popular Video Conferencing Applications

arXiv: https://arxiv.org/abs/2105.13478 · HTML: https://mlsanity.com/paper/2105.13478/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2021.

## Built on by (most cited first, 10 of 10)

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- [Video QoE Metrics from Encrypted Traffic: Application-agnostic Methodology](https://mlsanity.com/paper/2504.14720/) (2025)
- [Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement](https://mlsanity.com/paper/2509.19669/) (2025)
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## Abstract (arXiv)

Video conferencing applications (VCAs) have become a critical Internet application, even more so during the COVID-19 pandemic, as users worldwide now rely on them for work, school, and telehealth. It is thus increasingly important to understand the resource requirements of different VCAs and how they perform under different network conditions, including: how much speed (upstream and downstream throughput) a VCA needs to support high quality of experience; how VCAs perform under temporary reductions in available capacity; how they compete with themselves, with each other, and with other applications; and how usage modality (e.g., number of participants) affects utilization. We study three modern VCAs: Zoom, Google Meet, and Microsoft Teams. Answers to these questions differ substantially depending on VCA. First, the average utilization on an unconstrained link varies between 0.8 Mbps and 1.9 Mbps. Given temporary reduction of capacity, some VCAs can take as long as 50 seconds to recover to steady state. Differences in proprietary congestion control algorithms also result in unfair bandwidth allocations: in constrained bandwidth settings, one Zoom video conference can consume more than 75% of the available bandwidth when competing with another VCA (e.g., Meet, Teams). For some VCAs, client utilization can decrease as the number of participants increases, due to the reduced video resolution of each participant's video stream given a larger number of participants. Finally, one participant's viewing mode (e.g., pinning a speaker) can affect the upstream utilization of other participants.
