Neural Network Model of QoE for Estimation Video Streaming over 5G network

International audienceWith the rapid increasing demand of commercial video streaming, the satisfaction of the end user is becoming more and more important to measure and assure.The quality of experience (QoE) is defined as the measure of the overall level of customer satisfaction with the usage of a service provided by a vendor. Many works have addressed this issue in many different scenarios in cellular networks however most of these works have addressed video streaming over LTE networks (Long Term Evolution network). Up to day, there are few contributions of work that address the QoE over 5G network since there still still some challenges in this later to address. In this paper, we present the specific aspects we consider important in the evolution from 4G to 5G in term of traffic management and a solution to estimate this QoE in this new context. We adopted an approach based on Neural Network (NN) to estimate the QoE parameters. NN have been successfully used in many domains where it was difficult to derive an exact analytical model of the system so is the case of the 5G network

A multi-layer probing approach for video over 5G in vehicular scenarios

Fifth generation (5G) technologies are becoming a reality throughout the world. In parallel, vehicular networks rise their pace in terms of utilization; moreover, multimedia content transmissions are also getting an always increasing demand by their users. Besides the promised performance of 5G networks, several questions still arise among the community: are these networks capable of delivering high quality video streaming services in moving scenarios? What is the relationship between the network conditions and the video quality of experience? To answer to the previous questions, in this paper we propose a multi-layer probing approach able to assess video transmissions over 5G and 4G, combining data from all layers of a communication model, relating events from its origin layers. The probe's potential is thoroughly evaluated in two distinct video streaming use cases, both targeting a vehicular scenario supported by cellular 4G and 5G networks. Regarding the probe's performance, we show that a multitude of performance and quality indicators, from different stack layers, can be obtained. As for the performance of 4G and 5G networks in video streaming scenarios, the results have shown that the 5G links show a better overall performance in terms of video quality-of-experience, granting lower delays and jitter conditions, thus allowing video delay to be diminished and segment buffering to be better performed in comparison to 4G, while still showing adaptability in lightly traffic-saturated vehicular-to-vehicular scenarios.info:eu-repo/semantics/publishedVersio

Application of Quality of Experience in Networked Services: Review, Trend & Perspectives

Full text embargoed until 17.10.2019 (publisher's embargo period, 12 months

QoE in 5G Cloud Networks using Multimedia Services

The 4G standard Long Term Evolution-Advanced(LTE-A) has been deployed in many countries. Now, technologyis evolving towards the 5G standard since it is expecting tostart its service in 2020. The 5G cellular networks will mainlycontain in cloud computing and primarily Quality of Service(QoS) parameters (e.g. delay, loss rate, etc.) influence thecloud network performance. The impact of user perceivedQuality of Experience (QoE) using multimedia services, andapplication significantly relies on the QoS parameters. The keychallenge of 5G technology is to reduce the delay less thanone millisecond. In this paper, we have described a methodthat minimizes the overall network delay for multimediaservices; which are constant bit rate (VoIP) and variablebit rate (video) traffic model. We also proposed a methodthat measures the user’s QoE for video streaming trafficusing the network QoS parameters, i.e. delay and packet lossrate. The performance of proposed QoE method is comparedwith QoV method, and our proposed QoE method performsbest by carefully handle the impact of QoS parameters. Theresults show that our described method successfully reduces theoverall network delays, which result to maximize the user’s QoE

QoE in 5G Cloud Networks using Multimedia Services

The 4G standard Long Term Evolution-Advanced(LTE-A) has been deployed in many countries. Now, technologyis evolving towards the 5G standard since it is expecting tostart its service in 2020. The 5G cellular networks will mainlycontain in cloud computing and primarily Quality of Service(QoS) parameters (e.g. delay, loss rate, etc.) influence thecloud network performance. The impact of user perceivedQuality of Experience (QoE) using multimedia services, andapplication significantly relies on the QoS parameters. The keychallenge of 5G technology is to reduce the delay less thanone millisecond. In this paper, we have described a methodthat minimizes the overall network delay for multimediaservices; which are constant bit rate (VoIP) and variablebit rate (video) traffic model. We also proposed a methodthat measures the user’s QoE for video streaming trafficusing the network QoS parameters, i.e. delay and packet lossrate. The performance of proposed QoE method is comparedwith QoV method, and our proposed QoE method performsbest by carefully handle the impact of QoS parameters. Theresults show that our described method successfully reduces theoverall network delays, which result to maximize the user’s QoE

Modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicio de telecomunicaciones OTT-Telco

ANTECEDENTES El aprovisionamiento de la Calidad de la Experiencia (QoE) en servicios de telecomunicaciones requiere de sistemas de gestión que permitan monitorizar y controlar la QoE de los usuarios luego de consumir diferentes servicios de internet provistos sobre la red del operador. En efecto, el consumo elevado de datos por parte de los usuarios requiere, a nivel de gestión de la red, la asignación de recursos suficientes para el correcto funcionamiento de los servicios. En particular, la configuración de la Calidad del Servicio (QoS) ofrecida por el operador dentro de su dominio de operación se torna fundamental para proveer un tratamiento apropiado del tráfico, permitiendo que la percepción de la calidad del servicio por parte de los usuarios finales pueda mantenerse dentro del umbral de tolerancia de acuerdo con las políticas establecidas por la compañía de telecomunicaciones (Telco). En consecuencia, un modelo de correlación QoS-QoE es clave en el aprovisionamiento de servicios de internet sobre la infraestructura del operador de telecomunicaciones. OBJETIVOS La presente tesis de doctorado se centra en proponer un modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicios de telecomunicaciones OTT-Telco. Para ello, cinco acciones generales deben llevarse a cabo; a saber: () caracterizar los parámetros de QoS que mayor efecto tienen en la degradación de servicios OTT. () determinar las características, condiciones, parámetros y medidas de QoE en la prestación de un servicio OTT. () establecer las condiciones y restricciones de prestación de un servicio OTT en la infraestructura de una Telco que mantenga una buena relación QoS-QoE. () desarrollar un mecanismo de estimación o predicción de QoE con base en los factores de influencia de QoS que afectan la prestación de un servicio OTT. () evaluar experimentalmente el modelo de correlación QoE-QoS. MÉTODOS Para el cumplimiento de los objetivos, se definió un modelo integrado por un macro-componente Conceptualización y otro Operacional. El macro-componente Conceptualización está orientado por el referente metodológico para la construcción de marcos conceptuales de Jabareen, y el macro-componente Operacional está alineado con las fases definidas para el desarrollo de proyectos de minería de datos, CRISP-DM. Adicionalmente, se emplearon diseños de comprobación para los algoritmos, con el fin de comprobar la validez del modelo de estimación basado en algoritmos de aprendizaje automático; es decir, el modelo de estimación fue evaluado a partir de un diseño de comprobación donde se definen, para cada uno de los algoritmos, los parámetros iniciales de operación, las configuraciones de las diferentes pruebas, y las métricas usadas para evaluar su desempeño. RESULTADOS Los resultados más importantes alcanzados son los siguientes: un mapa estratégico del estado de la ciencia en el aprovisionamiento de la QoE para servicios OTT, una conceptualización de los perfiles del modelo de correlación, un modelo matemático para la valoración de la QoE de acuerdo con el comportamiento de consumo de los usuarios, un conjunto de datos de tráfico etiquetado que relaciona el comportamiento de la red con la percepción de la calidad de los usuarios, y un modelo de estimación de la QoE de los usuarios a partir del comportamiento de tráfico de la red. CONCLUSIONES El modelo de correlación QoS-QoE puede ser empleado en sistemas gestión de la QoE donde se requiere por parte de la Telco un diagnóstico y monitorización más objetiva de la percepción de la calidad del servicio por parte de sus usuarios dentro su red de aprovisionamiento. De igual manera, el empleo de parámetros adicionales de contexto de usuario enriquecería los sistemas de gestión de la QoE en el aprovisionamiento de servicios OTT.BACKGROUND Quality of Experience (QoE) provisioning requires robust QoE-centric network and application management on Telco network for providing internet services. Indeed, traffic growth over Telco network demands resource allocation for service well performance. Particularly, Quality of Service (QoS) configuration offered by network provider operational domain becomes a key component for traffic control in a proper manner. Hence, the quality of services perceived can be managed within a tolerance threshold according to telecom operator policies. Therefore, a QoS-QoE correlational model for internet services provisioning over the telecom operator infrastructure is required. AIMS The doctoral thesis is focused on propose a correlation QoS-QoE model for provisioning telecommunications services in OTT-Telco context. To this end, five goals must be accomplishing. () To characterize QoS parameters that more impact have on OTT services performance. () To determinate QoE assumptions, features, parameters, and metrics for OTT service provisioning. () To establish the assumptions and restrictions for providing a well QoS-QoE relation in the telecom operator. () To develop an estimation model for QoE based on QoS factors in the OTT services provisioning. () To evaluate the correlation QoS-QoE model. METHODS To accomplish the aims, a model with a Conceptual and Operational macro-component was structured. The Conceptual macro-component is based on the principles for building conceptual frameworks by Jabareen, and an Operational macro-component aligned with data mining project development phases, CRISP-DM. Furthermore, test bed design was structured to validate the estimation model base on machine learning algorithms; namely, algorithms initial parameters, some tests setup, and regression metrics were determined on a test bed for validate the performance of the estimation model proposed RESULTS The most relevant results achieved are the following: a strategic science map in the QoE provisioning for OTT services, three conceptual profiles as part of the correlation QoS-QoE model, a mathematical model for QoE assessment according to user consumption behavior, a label traffic dataset that relates the traffic network with quality of services perception, and estimation QoE model for users based on traffic flows. CONCLUSIONS The QoS-QoE correlational model can be applied in QoE-Driven application and network management in which an objective controlling and monitoring of quality of services perception by users is required. Moreover, additional user context parameters could be taking account for improving the QoE management systems in OTT services provisioning.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Jesús García Herrero.- Secretario: José Armando Ordóñez Córdoba.- Vocal: Juan Carlos Cuéllar Quiñóne