QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

Daengsi, Therdpong; Wuttidittachotti, Pongpisit

doi:10.1007/s10922-018-09487-4

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…For model evaluation, Mean Absolute Percentage Error (MAPE) that is a popular technique with high reliability has been selected for this article. It can be calculated using (15), where MAPE is the mean absolute percentage error (%), S cal means the calculated score from the mathematical model, and S ref is the reference score from the test set [ 11 , 35 ]. The MAPE value can be interpreted as follows: 0% < MAPE <10% presents an excellent forecast 10% < MAPE <20% presents a good forecast 20% < MAPE <50% presents a fair forecast …”

Section: Methodsmentioning

confidence: 99%

“…The average MAPE values from all mathematical models don’t seem different. Therefore, this is necessary to verify this by using a statistical technique called Analysis of Variance (ANOVA) as in [ 11 ]. For analysis using this technique, the raw data of MOS Audiovisual values from calculation with T1-T3 using (8)–(9) and (12)–(14) have been utilized based on the hypothesis as follows: H 0 : The audiovisual quality calculated from (8)–(9) and (12)–(14) is the same.…”

Section: Methodsmentioning

confidence: 99%

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QoE modeling for audiovisual associated with MOBA game using subjective approach

Pornpongtechavanicha

Wuttidittachotti

Daengsi

2022

Multimed Tools Appl

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With the advancement of multimedia communications and information technologies, online video games have become very popular recreational activities worldwide, while Multiplayer Online Battle Arena (MOBA) games have gained immense popularity from game players in recent times. Therefore, based on quality of experience, audiovisual quality evaluation models for MOBA games have been proposed in this study. The subjective data from 210 game players (both males and females) about audio quality during conversation, the video quality, the overall quality, and the weighted percentage of video and voice have been collected from small competition events of the Garena ROV (Arena of Valor) in Hua Hin, Thailand. The data from 160 game players have been used to create the models, while the data from 50 game players have been applied for model performance evaluation. After developing the models, Mean Absolute Percentage Error (MAPE) technique has been utilized for model evaluation. On completion of this study, it was found that two thirds of the proposed models provide better performance than the other previous works. All of them, called MOS Audiovisual-MOBA1 model, MOS Audiovisual-MOBA2 model and MOS Audiovisual-MOBA3 model, show excellent performances with MAPE values of 4.95%, 5.92% and 4.75% respectively. Therefore, excellent performance of these proposed models becomes one of the contributions of this study. Also, this evidence of excellent model performance makes the proposed models ideal for utilization for assessment of audiovisual quality of the MOBA games. All audiovisual quality evaluation models provide excellent efficiencies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

QoE modeling for audiovisual associated with MOBA game using subjective approach

Pornpongtechavanicha

Wuttidittachotti

Daengsi

2022

Multimed Tools Appl

Self Cite

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“…It is generally known that a real-time service that is very important for an enormous number of users, voice transmission over Internet protocol (VoIP), has been extensively researched with different goals. In [16], QoE modeling for VoIP service is presented by improving the simplified E-model with a subjective MOS model. The advantages of the final model in terms of accuracy and reliability are explained.…”

Section: Review Of Relevant Published Researchmentioning

confidence: 99%

Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

et al. 2022

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The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of QoE, this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal–regulatory, technological–process, content-formatted and performative, contextual–relational and subjective user-influencing factors on QoE sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark QoE as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of QoE. The hypothetical setting is that the individual user’s curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable QoE model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&H. By treating the data with mathematical and machine learning models, the QoE assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of QoEi. The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%.

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“…The model aimed to estimate the MOS scale based on the user-perceived web and video qualities. In [6], a simplified E-model based on a subjective MOS prediction model is proposed. This model aimed to enhance the objective measurement tool for specific codecs.…”

Section: Introductionmentioning

confidence: 99%

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

El‐Saleh¹,

Alhammadi²,

Shayea³

et al. 2022

Computers, Materials &Amp; Continua

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Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultralow latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.

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QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

Cited by 10 publications

References 35 publications

QoE modeling for audiovisual associated with MOBA game using subjective approach

QoE modeling for audiovisual associated with MOBA game using subjective approach

Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

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