Predicting quality of experience of popular mobile applications from a living lab study
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
In this paper, we present a hybrid method (qualitative and quantitative) to model and predict the Quality of Experience (QoE) of mobile applications used on WiFi or cellular network. Our 33 living lab participants rated their mobile applications' QoE in various contexts for four weeks resulting in a total of 5663 QoE ratings. At the same time, our smartphone logger (mQoL-Log) collected background information such as network information, user activity, battery statistics and more. We focused this study on frequently used and highly interactive applications including Google Chrome, Google Maps, Spotify, Instagram, Facebook, Facebook Messenger and WhatsApp. After pre-processing the dataset, we used classical machine learning techniques and algorithms (Extreme Gradient Boosting) to predict the QoE of the application usage. The results showed that our model can predict the user QoE with 94 0.77 accuracy. Surprisingly, after the following top three features:± session length, battery level and network QoS, the user activity (e.g., if walking) and intended action to accomplish with the app were the most predictive features. Longer application use sessions often have worse QoE than shorter sessions.
Original language | English |
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Title of host publication | 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) |
Number of pages | 5 |
Publisher | IEEE |
Publication date | 2019 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 11th International Conference on Quality of Multimedia Experience, QoMEX 2019 - Berlin, Germany Duration: 5 Jun 2019 → 7 Jun 2019 |
Conference
Conference | 11th International Conference on Quality of Multimedia Experience, QoMEX 2019 |
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Land | Germany |
By | Berlin |
Periode | 05/06/2019 → 07/06/2019 |
Sponsor | Bitmovin, Crowdee GmbH, Huawei, Telekom Innovation Laboratories, Youtube |
- Context, Mobile Applications, QoE Prediction, Quality of Experience, Quality of Service
Research areas
ID: 235477183