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Lightweight, General Inference of Streaming Video Quality from Encrypted Traffic

Abstract : Accurately monitoring application performance is becoming more important for Internet Service Providers (ISPs), as users increasingly expect their networks to consistently deliver acceptable application quality. At the same time, the rise of end-to-end encryption makes it difficult for network operators to determine video stream quality-including metrics such as startup delay, resolution, rebuffering, and resolution changes-directly from the traffic stream. This paper develops general methods to infer streaming video quality metrics from encrypted traffic using lightweight features. Our evaluation shows that our models are not only as accurate as previous approaches , but they also generalize across multiple popular video services, including Netflix, YouTube, Amazon Instant Video, and Twitch. The ability of our models to rely on lightweight features points to promising future possibilities for implementing such models at a variety of network locations along the end-to-end network path, from the edge to the core.
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Submitted on : Wednesday, March 20, 2019 - 11:39:20 PM
Last modification on : Friday, January 21, 2022 - 3:18:42 AM
Long-term archiving on: : Friday, June 21, 2019 - 10:29:30 PM


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  • HAL Id : hal-02074823, version 1
  • ARXIV : 1901.05800


Francesco Bronzino, Paul Schmitt, Sara Ayoubi, Nick Feamster, Renata Teixeira, et al.. Lightweight, General Inference of Streaming Video Quality from Encrypted Traffic. 2019. ⟨hal-02074823⟩



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