  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 just detecting short
versus long delays) allowing to draw better conclusions on the ongoing
streaming quality. Fourth, we demonstrate the model is 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.
