1.
Borchert, K., Seufert, A., Gamboa, E., Hirth, M., Hoßfeld, T.: In vitro vs in vivo: does the study’s interface design influence crowdsourced video QoE?. Quality and User Experience. (2020).
Evaluating the Quality of Experience (QoE) of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale. In general, most of these crowdsourcing studies either use, what we refer to, as an in vivo or an in vitro interface design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these interface designs vary widely, the results are often compared and generalized. In this work, we use a crowdsourcing study to investigate the influence of three interface design alternatives, an in vitro and two in vivo designs with different levels of interactiveness, on the perceived video QoE. Contrary to our expectations, the results indicate that there is no significant influence of the study’s interface design in general on the video experience. Furthermore, we found that the in vivo design does not reduce the test takers’ attentiveness. However, we observed that participants who interacted with the test interface reported a higher video QoE than other groups.
2.
Schwind, A., Wamser, F., Hoßfeld, T., Wunderer, S., Tarnvik, E., Hall, A.: Crowdsourced Network Measurements in Germany: Mobile Internet Experience from End User Perspective. Broadband Coverage in Germany; 14. ITG Symposium. , Berlin (2020).
Collecting and analyzing meaningful data in mobile networks is the key to assessing network performance. Crowdsourced Network Measurements (CNMs) provide insights beyond the network layer and offer performance and other measurements at the application and user-level towards Quality of Experience (QoE). In this paper, the mobile Internet experience for Germany is evaluated with the help of crowdsourcing from the perspective of an end user. We statistically analyze a dataset with throughput measurements on the end device from Tutela Ltd., which covers more than 2.5 million throughput tests across Germany from January to July 2019. We give insights into this emerging methodology and highlight the benefits of this method. The paper contains statistics and conclusions for several large cities as well as regions in Germany compared to general statements for Germany, since individual measurements and averages often only imprecisely reflect the situation. The goal is to give a holistic view of the performance of the current mobile network in Germany. Reading this paper, it becomes evident that reliable statements about the quality of the mobile network for Germany depend on a large number of peculiarities in different regions with their own performance characteristics due to different network deployments and population numbers.
3.
Schwind, A., Moldovan, C., Janiak, T., Dworschak, N.-D., Hoßfeld, T.: Don’t Stop the Music: Crowdsourced QoE Assessment of Music Streaming with Stalling. 12th International Conference on Quality of Multimedia Experience (QoMEX). , Athlone, Ireland (2020).
Streaming made a lasting effect on the way our society consumes media in the last decade. While due to streaming the way we listen to music and podcasts has changed drastically, there are very few studies about its Quality of Experience (QoE) and possible influence factors. From video QoE studies, we know that, for example, undesirable stops of the stream (stalling events) have a significant impact on QoE. However, the way in which music and video streaming is consumed differs significantly, as music is often played in the background, and thus, the influence of stalling could be significantly different. Thus, this work evaluates the impact of stalling on music streaming QoE. Therefore, we conduct two crowdsourced user studies: In the first study, users have to rate four songs with different stalling patterns and evaluate the degree of impairments. Afterwards, we compare the ratings to the results of a lab study and show that they are highly correlated, and that crowdsourcing is a suitable way of measuring music streaming QoE. In addition, we conduct a second crowdsourcing study to investigate the influence of the user's attentiveness on QoE. Here, participants have to listen to one song with two stalling events, while one half of them had to transcribe a handwritten text with music playing in the background. The attentiveness shows no influence on the perceived streaming quality, but it shows a significant influence on the perceived quality degradation due to stalling events. Furthermore, considerably more stalling events were missed for workers who focused on the transcription. These results are an important step towards establishing new methods for investigating QoE in multimedia.
4.
Hoßfeld, T., Wunderer, S., Beyer, A., Hall, A., Schwind, A., Gassner, C., Guillemin, F., Wamser, F., Wascinski, K., Hirth, M., Seufert, M., Casas, P., Tran-Gia, P., Robitza, W., Wascinski, W., Ben Houidi, Z.: White Paper on Crowdsourced Network and QoE Measurements – Definitions, Use Cases and Challenges. Würzburg (2020).
The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements.
5.
Schwind, A., Haberzettl, L., Wamser, F., Hoßfeld, T.: QoE Analysis of Spotify Audio Streaming and App Browsing. 4th Internet-QoE Workshop: QoE-based Analysis and Management of Data Communication Networks (Internet-QoE’19) (2019).
Spotify is the most-listened audio streaming provider in 2019 with 217 million active users per month. Providers are therefore interested in the quality and functionality of Spotify in order to provide their users with the best possible streaming quality. While video streaming services such as Netflix and their streaming approach have been extensively explored in previous research, audio streaming services like Spotify and their corresponding behavior at certain network conditions have not been considered in detail yet. In this paper, we perform a QoE analysis under various network conditions and examine the app browsing performance of the audio streaming platform Spotify using its native Android mobile application. We have developed a measurement tool that emulates a user listening to audio through Spotify. While streaming, application and network layer parameters are captured that have a high correlation to the user’s QoE. The paper shows a baseline scenario including the streaming of a single song as well as playlist streaming behavior. Next, the effect of interruptions on the streaming behavior is evaluated and finally, the influence of network impairments on QoE key performance indicators such as initial delay is shown.