On User-Centric Modular QoE Prediction for VoIP Based on Machine-Learning Algorithms

The impact of the network performance on the quality of experience (QoE) for various services is not well-understood. Assessing the impact of different network and channel conditions on the user experience is important for improving the telecommunication services. The QoE for various wireless servic...

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Vydané v:IEEE transactions on mobile computing Ročník 15; číslo 6; s. 1443 - 1456
Hlavní autori: Charonyktakis, Paulos, Plakia, Maria, Tsamardinos, Ioannis, Papadopouli, Maria
Médium: Magazine Article
Jazyk:English
Vydavateľské údaje: Los Alamitos IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Classifiers
Estimation
Loss measurement
Modular
Networks
Neural networks
Packets (communication)
Prediction algorithms
quality of experience
Quality of service
Telephones
Training
Voice
VoIP
VoIP (protocol)
Wireless networks
modeling
PESQ
VoIP
wireless networks
user-centric
machine learning
statistical analysis
E-model
quality of experience
ISSN:1536-1233, 1558-0660
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Popis
Shrnutí:The impact of the network performance on the quality of experience (QoE) for various services is not well-understood. Assessing the impact of different network and channel conditions on the user experience is important for improving the telecommunication services. The QoE for various wireless services including VoIP, video streaming, and web browsing, has been in the epicenter of recent networking activities. The majority of such efforts aim to characterize the user experience, analyzing various types of measurements often in an aggregate manner. This paper proposes the MLQoE, a modular algorithm for user-centric QoE prediction. The MLQoE employs multiple machine learning (ML) algorithms, namely, Artificial Neural Networks, Support Vector Regression machines, Decision Trees, and Gaussian Naive Bayes classifiers, and tunes their hyper-parameters. It uses the Nested Cross Validation (nested CV) protocol for selecting the best classifier and the corresponding best hyper-parameter values and estimates the performance of the final model. The MLQoE is conservative in the performance estimation despite multiple induction of models. The MLQoE is modular, in that, it can be easily extended to include other ML algorithms. The MLQoE selects the ML algorithm that exhibits the best performance and its parameters automatically given the dataset used as input. It uses empirical measurements based on network metrics (e.g., packet loss, delay, and packet interarrival) and subjective opinion scores reported by actual users. This paper extensively evaluates the MLQoE using three unidirectional datasets containing VoIP calls over wireless networks under various network conditions and feedback from subjects (collected in field studies). Moreover, it performs a preliminary analysis to assess the generality of our methodology using bidirectional VoIP and video traces. The MLQoE outperforms several state-of-the-art algorithms, resulting in fairly accurate predictions.
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ISSN:1536-1233
1558-0660
DOI:10.1109/TMC.2015.2461216