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Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality-of-Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the wellknown SpeedIndex (SI) metric. Given the lack of applicationlevel-data visibility introduced by the wide adoption of end-toend encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web-page loading sessions, using as input only packet-and flow-level data. In this paper, we study the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web-QoE measurements for the most popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packetlevel or flow-level measurements. In addition, we show that the device type adds a strong bias to the feasibility of these Web-QoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of the art, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web-QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios.

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

Web browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machinelearning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem.