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Inferring the quality of streaming video applications is important forInternet service providers, but the fact that most video streams are encryptedmakes it difficult to do so. We develop models that infer quality metrics (\ie,startup delay and resolution) for encrypted streaming video services. Our paperbuilds on previous work, but extends it in several ways. First, the model worksin deployment settings where the video sessions and segments must be identifiedfrom a mix of traffic and the time precision of the collected trafficstatistics is more coarse (\eg, due to aggregation). Second, we develop asingle 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 finergranularity (\eg, the precise startup delay instead of just detecting shortversus long delays) allowing to draw better conclusions on the ongoingstreaming quality. Fourth, we demonstrate the model is practical through a16-month deployment in 66 homes and provide new insights about therelationships between Internet "speed" and the quality of the correspondingvideo streams, for a variety of services; we find that higher speeds provideonly minimal improvements to startup delay and resolution.

Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience