Measuring the Performance and Network Utilization of Popular Video Conferencing Applications
Not on the map. Why?
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 6.
10 papers.
- 2023GRACE: Loss-Resilient Real-Time Video through Neural Codecscited by 9
- 2025FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packetscited by 3
- 2024Eloquent: A More Robust Transmission Scheme for LLM Token Streamingcited by 2
- 2025Demystifying Network Foundation Modelscited by 2
- 2024ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysiscited by 1
- 2024Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communicationscited by 1
- 2025Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streamscited by 0
- 2025Video QoE Metrics from Encrypted Traffic: Application-agnostic Methodologycited by 0
- 2025Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurementcited by 0
- 2025Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Appscited by 0
- 2025Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps1 Oct 2025
- 2025Demystifying Network Foundation Models27 Sep 2025
- 2025Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement24 Sep 2025
- 2025Video QoE Metrics from Encrypted Traffic: Application-agnostic Methodology20 Apr 2025
- 2025FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets2 Apr 2025
- 2025Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams7 Mar 2025
- 2024Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications21 Oct 2024
- 2024ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis6 Feb 2024
- 2024Eloquent: A More Robust Transmission Scheme for LLM Token Streaming23 Jan 2024
- 2023GRACE: Loss-Resilient Real-Time Video through Neural Codecs21 May 2023