Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation
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2021
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Abstract
Social media, professional sports, and video games are driving rapid growth
in live video streaming, on platforms such as Twitch and YouTube Live. Live
streaming experience is very susceptible to short-time-scale network congestion
since client playback buffers are often no more than a few seconds.
Unfortunately, identifying such streams and measuring their QoE for network
management is challenging, since content providers largely use the same
delivery infrastructure for live and video-on-demand (VoD) streaming, and
packet inspection techniques (including SNI/DNS query monitoring) cannot always
distinguish between the two.
In this paper, we design, build, and deploy ReCLive: a machine learning
method for live video detection and QoE measurement based on network-level
behavioral characteristics. Our contributions are four-fold: (1) We analyze
about 23,000 video streams from Twitch and YouTube, and identify key features
in their traffic profile that differentiate live and on-demand streaming. We
release our traffic traces as open data to the public; (2) We develop an
LSTM-based binary classifier model that distinguishes live from on-demand
streams in real-time with over 95% accuracy across providers; (3) We develop a
method that estimates QoE metrics of live streaming flows in terms of
resolution and buffer stall events with overall accuracies of 93% and 90%,
respectively; and (4) Finally, we prototype our solution, train it in the lab,
and deploy it in a live ISP network serving more than 7,000 subscribers. Our
method provides ISPs with fine-grained visibility into live video streams,
enabling them to measure and improve user experience.
| Reference Key |
sivaraman2021modeling
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| Authors | Sharat Chandra Madanapalli; Alex Mathai; Hassan Habibi Gharakheili; Vijay Sivaraman |
| Journal | arXiv |
| Year | 2021 |
| DOI |
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