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Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live video streaming by developing dynamic models and optimal control strategies. We further develop practical live video streaming algorithms within the Model Predictive Control (MPC) framework, namely MPC-Live, to maximize user QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live video streaming algorithms can improve the performance dramatically within latency range of two to five seconds.
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A Measurement-Derived Functional Model for the Interaction Between Congestion Control and QoE in Video Conferencing
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.
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- Award ID(s):
- 1909040
- PAR ID:
- 10577252
- Publisher / Repository:
- Springer
- Date Published:
- ISSN:
- 0302-9743
- ISBN:
- 978-3-031-28485-4
- Format(s):
- Medium: X
- Location:
- Virtual
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
