1.
Seufert, A., Schweifler, R., Poignée, F., Seufert, M., Hoßfeld, T.: Waiting along the Path: How Browsing Delays Impact the QoE of Music Streaming Applications. 14th International Conference on Quality of Multimedia Experience (QoMEX) (2022).
Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming applications.
2.
Wehner, N., Amir, M., Seufert, M., Schatz, R., Hoßfeld, T.: A Vital Improvement? Relating Google’s Core Web Vitals to Actual Web QoE. 14th International Conference on Quality of Multimedia Experience (QoMEX) (2022).
Providing sophisticated Web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in Web technologies (HTML5, responsive design, etc.), traditional Web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of Web QoE. To this end, we first perform objective measurements in the Web using Google's Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals. Our results suggest that the Core Web Vitals have less predictive value for Web QoE than expected and that page loading times remain the main influence factor in this context.
3.
Casas, P., Wassermann, S., Seufert, M., Wehner, N., Dinica, O., Hoßfeld, T.: X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning. Network Traffic Measurement and Analysis Conference (TMA) (2022).
The wide adoption of end-to-end encryption is drastically limiting the visibility Internet Service Providers (ISPs) have on the performance of the services consumed by their customers. In times of strong competition, where customer experience plays a key role in churn management, ISPs require novel solutions enabling network-wide Quality of Experience (QoE) monitoring. To this end, we present DeepQoE, a deep-learning based approach to infer the QoE of web services and mobile applications from the ISP perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived from the encrypted stream of bytes as input to deep Convolutional Neural Networks (CNNs), DeepQoE infers the Speed Index of web browsing sessions and general mobile apps with unprecedented accuracy, improving the state of the art by more than 25%, and reducing the QoE inference error in terms of mean opinion scores by nearly 40%. DeepQoE implements a web fingerprinting solution to identify individual web browsing sessions within concurrent web pages traffic, enabling highly detailed, per web page QoE inference in practical deployments. Extensive evaluations over a large and heterogeneous dataset composed of web and app measurements, using different device types and for top-popular websites and apps, confirm the out-performance of DeepQoE over previously used shallow-learning models, as well as the deep-model generalization to different devices, web pages, apps, and network setups. DeepQoE is the first deployable system providing such a deep, highly-detailed QoE for individual web browsing and mobile apps over encrypted traffic, using deep learning models on heterogeneous measurements.