Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience
Paul Schmitt, Francesco Bronzino, Sara Ayoubi, Guilherme Martins,, Renata Teixeira, Nick Feamster

TL;DR
This paper presents practical models for inferring streaming video quality metrics from encrypted traffic, applicable across multiple services, and validated through a 16-month deployment revealing insights on Internet speed and video quality.
Contribution
Develops a unified, deployment-ready model for estimating video quality from encrypted traffic across various services with finer granularity and real-world validation.
Findings
Models work effectively in coarse, real-world traffic conditions.
A single model applies to multiple streaming services.
Higher Internet speeds yield minimal improvements in video quality.
Abstract
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 model works 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 (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, the model performs predictions at finer granularity (\eg, the precise startup delay instead of…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Image and Video Quality Assessment · Caching and Content Delivery
