As video traffic dominates the Internet, it is important for operators
to detect video Quality of Experience (QoE) in order to ensure
adequate support for video traffic. With wide deployment of endto-
end encryption, traditional deep packet inspection based traffic
monitoring approaches are becoming ineffective. This poses a challenge
for network operators to monitor user QoE and improve upon
their experience. To resolve this issue, we develop and present a
system for REal-time QUality of experience metric detection for
Encrypted Traffic, Requet. Requet uses a detection algorithm we
develop to identify video and audio chunks from the IP headers of
encrypted traffic. Features extracted from the chunk statistics are
used as input to a Machine Learning (ML) algorithm to predict QoE
metrics, specifically, buffer warning (low buffer, high buffer), video
state (buffer increase, buffer decay, steady, stall), and video resolution.
We collect a large YouTube dataset consisting of diverse video
assets delivered over various WiFi network conditions to evaluate
the performance. We compare Requet with a baseline system based
on previous work and show that Requet outperforms the baseline
system in accuracy of predicting buffer low warning, video state,
and video resolution by 1.12×, 1.53×, and 3.14×, respectively.
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Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience
Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. We develop models that infer quality metrics (\ie, startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (\eg, due to aggregation). Second, we develop a single composite model that works for a range of different services (\ie, Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (\eg, the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed'' and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.
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- Award ID(s):
- 1704077
- PAR ID:
- 10262199
- Date Published:
- Journal Name:
- Proceedings of the ACM on Measurement and Analysis of Computing Systems
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2476-1249
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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