Mobile QoE prediction in the field

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Mobile QoE prediction in the field. / Boz, E.; Finley, B.; Oulasvirta, A.; Kilkki, K.; Manner, J.

In: Pervasive and Mobile Computing, Vol. 59, 101039, 01.10.2019.

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@article{99815584520946d79cba8a227b0db7dd,
title = "Mobile QoE prediction in the field",
abstract = "Quality of experience (QoE) models quantify the relationship between user experience and network quality of service. With the exception of a few studies, most research on QoE has been conducted in laboratory conditions. Therefore, in order to validate and develop QoE models for the wild, researchers should carry out large scale field studies. This paper contributes data and observations from such a large-scale field study on mobile devices carried out in Finland with 292 users and 64,036 experience ratings. 74{\%} of the ratings are associated with Wifi or LTE networks. We report descriptive statistics and classification results predicting normal vs. bad QoE in in-the-wild measurements. Our results illustrate a 20{\%} improvement over baselines for standard classification metrics (G-Mean). Furthermore, both network features (such as delay) and non-network features (such as device memory) show importance in the models. The models’ performance suggests that mobile QoE prediction remains a difficult problem in field conditions. Our results help inform future modeling efforts and provide a baseline for such real-world mobile QoE prediction.",
keywords = "Hybrid measurements, Network monitoring, Quality of experience",
author = "E. Boz and B. Finley and A. Oulasvirta and K. Kilkki and J. Manner",
year = "2019",
month = "10",
day = "1",
doi = "10.1016/j.pmcj.2019.101039",
language = "English",
volume = "59",
journal = "Pervasive and Mobile Computing",
issn = "1574-1192",
publisher = "Elsevier",

}

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TY - JOUR

T1 - Mobile QoE prediction in the field

AU - Boz, E.

AU - Finley, B.

AU - Oulasvirta, A.

AU - Kilkki, K.

AU - Manner, J.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Quality of experience (QoE) models quantify the relationship between user experience and network quality of service. With the exception of a few studies, most research on QoE has been conducted in laboratory conditions. Therefore, in order to validate and develop QoE models for the wild, researchers should carry out large scale field studies. This paper contributes data and observations from such a large-scale field study on mobile devices carried out in Finland with 292 users and 64,036 experience ratings. 74% of the ratings are associated with Wifi or LTE networks. We report descriptive statistics and classification results predicting normal vs. bad QoE in in-the-wild measurements. Our results illustrate a 20% improvement over baselines for standard classification metrics (G-Mean). Furthermore, both network features (such as delay) and non-network features (such as device memory) show importance in the models. The models’ performance suggests that mobile QoE prediction remains a difficult problem in field conditions. Our results help inform future modeling efforts and provide a baseline for such real-world mobile QoE prediction.

AB - Quality of experience (QoE) models quantify the relationship between user experience and network quality of service. With the exception of a few studies, most research on QoE has been conducted in laboratory conditions. Therefore, in order to validate and develop QoE models for the wild, researchers should carry out large scale field studies. This paper contributes data and observations from such a large-scale field study on mobile devices carried out in Finland with 292 users and 64,036 experience ratings. 74% of the ratings are associated with Wifi or LTE networks. We report descriptive statistics and classification results predicting normal vs. bad QoE in in-the-wild measurements. Our results illustrate a 20% improvement over baselines for standard classification metrics (G-Mean). Furthermore, both network features (such as delay) and non-network features (such as device memory) show importance in the models. The models’ performance suggests that mobile QoE prediction remains a difficult problem in field conditions. Our results help inform future modeling efforts and provide a baseline for such real-world mobile QoE prediction.

KW - Hybrid measurements

KW - Network monitoring

KW - Quality of experience

UR - http://www.scopus.com/inward/record.url?scp=85068129666&partnerID=8YFLogxK

U2 - 10.1016/j.pmcj.2019.101039

DO - 10.1016/j.pmcj.2019.101039

M3 - Article

VL - 59

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

M1 - 101039

ER -

ID: 35746760