Predicting QoE in cellular networks using machine learning and in-smartphone measurements

Casas, Pedro; D’Alconzo, Alessandro; Wamser, Florian; Seufert, Michael; Gardlo, Bruno; Schwind, Anika; Tran-Gia, Phuoc; Schatz, Raimund

doi:10.1109/qomex.2017.7965687

Cited by 68 publications

(61 citation statements)

References 16 publications

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“…Generally, these works focus on short-term QoE, i.e., they estimate the QoE of individual video sessions starting from flow-level features extracted from each video network traffic traces, such as flow size and duration, average throughput and statistics on RTT and packet losses. Network data is gathered either from network-side traffic traces or directly by user terminals [6]. QoE is obtained either directly through subjective user feedbacks in form of Mean Opinion Scores (MOS), or more often it is substituted by objective QoE metrics such as number of video stalls or buffering ratio [7], the downlink bandwidth or the access Round Trip Time [8], whose correlation to user satisfaction is well established [9].…”

Section: Related Workmentioning

confidence: 99%

Towards Long-Term Coverage and Video Users Satisfaction Prediction in Cellular Networks

Pimpinella

Redondi

Galimberti

et al. 2019

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)

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Network operators are interested in continuously monitoring the satisfaction of their customers to minimise the churn rate: however, collecting user feedbacks through surveys is a cumbersome task. In this work we explore the possibility of predicting the long-term user satisfaction relative to network coverage and video streaming starting from user-side network measurements only. We leverage country-wide datasets to engineer features which are then used to train several machine learning models. The obtained results suggest that, although some correlation is visible and could be exploited by the classifiers, long-term user satisfaction prediction from network measurements is a very challenging task: we therefore point out possible action points to be implemented to improve the prediction results.

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Section: Related Workmentioning

confidence: 99%

Towards Long-Term Coverage and Video Users Satisfaction Prediction in Cellular Networks

Pimpinella

Redondi

Galimberti

et al. 2019

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)

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“…YoMoApp [1] is an extension of our previous YoMo tool, but implemented as an Android app to passively measure YouTube QoE-relevant features in smartphones. Last, previous papers have also presented results on machine learning for QoE prediction in smartphones: our previous work [22], [23] as well as [21] use machine-learning models to infer the QoE of smartphone apps, relying on indevice and/or in-network measurements.…”

Section: Related Workmentioning

confidence: 99%

On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements

Wassermann

Casas

Seufert

et al. 2019

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)

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“…Examples of these techniques are Fuzzy Inference System (FIS), Decision Trees, Adaptive Neural Network (ANN), and Support Vector Machines, etc. [15]- [20]. The study in [15] presented a video quality estimation model using fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Model for Quality Estimation in Wireless 2D/3D Video Streaming Systems

Alsukayti*

2019

IJITEE

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Delivering high Quality of Experience (QoE) is essential to the success of today’s subscription for internet video streaming services. Quality of Service (QoS) metrics are considered by the research community as the most influential factor on video QoE. Therefore, establishing QoS-QoE correlation becomes critical for improving video QoE estimation. This paper presents experimental development of effective correlation between QoE and QoS for both 2D and 3D video streaming services. This is then used to build an objective QoE estimation model for real-time streaming of both 2D and 3D video contents over wireless networks. This model is based on using Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the perceived video QoE. The proposed QoE model was trained with a set of media and packet layers’ metrics, taking into account the effect of video content type, dimension, and different packet loss metrics. The performance of the proposed QoE estimation model shows a considerable estimation accuracy with a correlation coefficient of 92% and 0.167 RMSE.

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Predicting QoE in cellular networks using machine learning and in-smartphone measurements

Cited by 68 publications

References 16 publications

Towards Long-Term Coverage and Video Users Satisfaction Prediction in Cellular Networks

Towards Long-Term Coverage and Video Users Satisfaction Prediction in Cellular Networks

On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements

An Adaptive Neuro-Fuzzy Model for Quality Estimation in Wireless 2D/3D Video Streaming Systems

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