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

Paul Schmitt (1) , Francesco Bronzino (2) , Sara Ayoubi (2) , Guilherme Martins (1) , Renata Teixeira (3, 2) , Nick Feamster (1)

1 Princeton University
2 MIMOVE - Middleware on the Move
3 LINCS - Laboratory of Information, Network and Communication Sciences

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 (i.e., 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 (e.g., 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 (e.g., 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.

Domaines: Réseaux et télécommunications [cs.NI]; Interface homme-machine [cs.HC]

Paul Schmitt, Francesco Bronzino, Sara Ayoubi, Guilherme Martins, Renata Teixeira, et al.. Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience. 2019. ⟨hal-02268404⟩