A Novel Online QoE Prediction Model Based on Multiclass Incremental Support Vector Machine

Youssef, Yosr Ben; Afif, Mériem; Ksantini, Riadh; Tabbane, Sami

doi:10.1109/aina.2018.00058

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…However, for actual network management, it is not realistic to obtain the subjective experience of each user. Therefore, the objective part of user experience is the KQI, which is increasingly used for research on user experience estimation [ 21 , 22 , 24 , 25 ]. The prediction technology of the KPIs for wireless network is becoming mature, but the prediction value of the KPIs cannot well reflect the actual user experience; meanwhile, the KQI data are often unpredictable.…”

Section: Discussionmentioning

confidence: 99%

“…Among them, most research is based on discretized KQI data [ 21 ]. The effectiveness of the SVM [ 24 , 25 , 26 ] and Bayesian [ 13 ] classifiers has been verified. By comparing the proposed algorithm with these advanced methods, the results showed that the proposed algorithm can better deal with user experience estimation.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the complexity of KPI data, discretization will bring much inaccuracy. Some commonly used association methods, such as a priori [ 35 ], SVM [ 24 , 25 ], random forest [ 36 ], and so on, have a high complexity, and these methods cannot obtain real-time fault cause feedback.…”

Section: Methodsmentioning

confidence: 99%

“…In [ 23 ], a mapping function from the QoS to MOS was studied, and the authors derived corresponding QoE scores according to this user rating distribution. Y. Ben et al [ 24 ] proposed a novel online QoE estimation model, which used incremental multiclass SVM (multiclass-iSVM) to classify users’ perception of video streaming services. The proposed online QoE model studies the effectiveness of incremental learning to process large-scale dynamic data and improve the prediction accuracy of the QoE.…”

Section: Related Workmentioning

confidence: 99%

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User Experience Estimation in Multi-Service Scenario of Cellular Network

Zhang

Chuai

Maimaiti

et al. 2021

Sensors

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The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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User Experience Estimation in Multi-Service Scenario of Cellular Network

Zhang

Chuai

Maimaiti

et al. 2021

Sensors

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“…In [143], a no reference cross-layer end-to-end estimation model for mobile video perceptual quality is presented, based on random neural networks. In [144] an online QoE prediction model is proposed, capable of classifying user perception of video streaming services, based on incremental multiclass SVM (multiclass-iSVM) algorithm, which examines the efficacy of incremental learning in handling large scale dynamic data and improving QoE prediction accuracy. In [145], radio measurements of the wireless communication channel are considered, including the received signal strength indicator (RSSI), reference signal received power (RSRP), reference signal received quality (RSRQ), secondary synchronization signal power (SSSP), total reference signal power, channel quality indicator (CQI), modulation coding scheme (MCS) index, and carrier to interference plus noise ratio (CINR).…”

Section: A Video Streamingmentioning

confidence: 99%

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

et al. 2022

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The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-highdefinition video, and extended reality, requires the enhancement of end-users' perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user's personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.

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Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model

Liang

Wang

et al. 2020

Lecture Notes in Electrical Engineering

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A Novel Online QoE Prediction Model Based on Multiclass Incremental Support Vector Machine

Cited by 9 publications

References 21 publications

User Experience Estimation in Multi-Service Scenario of Cellular Network

User Experience Estimation in Multi-Service Scenario of Cellular Network

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model

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