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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VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building
We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets. VOCALExplore supports interactive labeling sessions and trains models using user-supplied labels. VOCALExplore maximizes model quality by automatically deciding how to select samples based on observed skew in the collected labels. It also selects the optimal video representations to use when training models by casting feature selection as a rising bandit problem. Finally, VOCALExplore implements optimizations to achieve low latency without sacrificing model performance. We demonstrate that VOCALExplore achieves close to the best possible model quality given candidate acquisition functions and feature extractors, and it does so with low visible latency (~1 second per iteration) and no expensive preprocessing.
more » « less- Award ID(s):
- 2211133
- PAR ID:
- 10531466
- Publisher / Repository:
- VLDB Endowment
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 16
- Issue:
- 13
- ISSN:
- 2150-8097
- Page Range / eLocation ID:
- 4188 to 4201
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.14778/3625054.3625057
