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

Chen

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

et al. 2019

This paper investigates the issue of Quality of Experience (QoE) for multimedia services in Internet of Things (IoT), introduces a new concept of "Isomorphism Flow"(iFlow) for multimedia traffic in IoT, which is inspired by abstract algebra based on experimental research and is aggregated from the multimedia traffics with similar Quality of Experience (QoE) requirements for different users, and improves typical QoE evaluation method for multimedia traffic in IoT.Then a new cross-domain QoE guarantee method based on the iFlow QoE is proposed in this paper to adjust the network resource from the perspective of user perception. The proposed scheme is validated through simulation, which indicates the proposed scheme outperforms the current scheme. Specifically, simulation results show that the proposed scheme achieves an enhancement in QoE performance for the multimedia traffics. Furthermore, it retains the perceived QoE of the real-time application users within high satisfaction levels.

Section: B Qoe Evaluation Methodsmentioning

confidence: 99%

Section: B Qoe Evaluationmentioning

confidence: 99%

New Cross-Domain QoE Guarantee Method Based on Isomorphism Flow

Chen

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

et al. 2019

Lecture Notes in Computer Science

“…While machine learning algorithms have been used to model QoE for VoIP [12], video streaming [6] or Skype [23], its application to Web browsing is still lacking. One marked exception is the work by Gao et al [15], where authors formulate a ternary classification task (i.e., A is faster, B is faster, none is faster) and employ Random Forest and Gradient Boosting ML techniques with QoS metrics such as those described in Section 2.1 as input features.…”

Section: Web Qoe Modelsmentioning

confidence: 99%

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

Hora

Asrese

Christophides

et al. 2018

Abstract. Page load time (PLT) is still the most common application Quality of Service (QoS) metric to estimate the Quality of Experience (QoE) of Web users. Yet, recent literature abounds with proposals for alternative metrics (e.g., Above The Fold, SpeedIndex and variants) that aim at better estimating user QoE. The main purpose of this work is thus to thoroughly investigate a mapping between established and recently proposed objective metrics and user QoE. We obtain ground truth QoE via user experiments where we collect and analyze 3,400 Web accesses annotated with QoS metrics and explicit user ratings in a scale of 1 to 5, which we make available to the community. In particular, we contrast domain expert models (such as ITU-T and IQX) fed with a single QoS metric, to models trained using our ground-truth dataset over multiple QoS metrics as features. Results of our experiments show that, albeit very simple, expert models have a comparable accuracy to machine learning approaches. Furthermore, the model accuracy improves considerably when building per-page QoE models, which may raise scalability concerns as we discuss.

“…Mitra et al proposed a CaQoEM approach, which incorporated Bayesian networks, utility theory, and bipolar scale to predict user QoE. Charonyktakis et al exploited artificial neural networks, support vector regression (SVR) machines, decision trees, and Gaussian naive Bayes classifiers to build QoE/QoS models for wireless services. Artificial neural networks are especially appealing to establish the complex and nonlinear relationship between QoS and QoE .…”

Section: Related Workmentioning

confidence: 99%

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Huang

2018

Int J Communication

Summary Satellite networks are promising alternatives to deal with the increasing traffic volume and provide universal access for end users. Quality of experience (QoE) as an aggregate of user perception and network quality will be a critical success factor for multimedia service delivery over satellite networks. Quality of experience prediction based on quality of service (QoS) has received much attention recently, but it has been little investigated in satellite networks. In this paper, on the basis of the modular neural network and deep belief networks (DBNs), we design a QoE/QoS mapping method for multimedia services over satellite networks to translate QoS parameters into user QoE. The task of QoE/QoS mapping for multimedia services is decomposed into several different subtasks by using traffic classification. Heterogeneous DBNs are used to learn the mapping relationships between QoE and QoS for different types of services (ie, subtask) simultaneously. An integrative approach based on relative distance is exploited to select subneural network(s) to predict resulting QoE collaboratively. To determine the weight parameters of the QoE/QoS correlation model, the dataset composed of QoS parameters and subjective opinion scores is built on the basis of satellite network simulator and subjective test. Finally, the effectiveness of our QoE/QoS mapping method is validated, and the relationship between adoption rating and opinion scores is analyzed.