Assessment of Quality of Experience (QoE) of Web and Video Services over a Mobile Network Using a Network Emulator

This paper investigates the subjective assessment of QoE of web and video services over a mobile network. To achieve this, the authors used the network emulator (NetEm) traffic control functionality to simulate the dynamic behaviour of a mobile network. Experiments were conducted in a laboratory setting and test conditions were varied to ascertain the QoE, with a focus on QoE metrics such as delay and packet loss ratio. From the experiments conducted, it was observed that there was a negative correlation between delay and average mean opinion score (MOS), and between packet loss ratio and average MOS. The results obtained can be adopted by network operators to provide better services which would lead to improved subscriber base and profitability for the operators and better QoE for the end users.

Heterogeneous Quality of Experience Guarantees Over Wireless Networks

Quality of experience(Qo E), which is very critical for the experience of users in wireless networks, has been extensively studied. However, due to different human perceptions, quantifying the effective capacity of wireless network subject to diverse Qo E is very difficult, which leads to many new challenges regarding Qo E guarantees in wireless networks. In this paper, we formulate the Qo E guarantees model for cellular wireless networks. Based on the model, we convert the effective capacity maximization problem into the equivalent convex optimization problem. Then, we develop the optimal Qo E-driven power allocation scheme, which can maximize the effective capacity. The obtained simulation results verified our proposed power allocation scheme, showing that the effective capacity can be significantly increased compared with that of traditional Qo E guarantees based schemes.

Quality of experience based scheduling algorithm in LTE network with various traffics

Quality of experience （ QoE ） based scheduling algorithm of long term evalution （ LTE ） network with various traffics is studied. Utility functions are adopted to estimate mean opinion score （MOS） for different traffics and a new MOS metric called normalized MOS is defined. A scheduling algorithm based on normalized MOS and greedy algorithm is proposed, aiming at maximizing the entirety MOS level of the whole users in the cell. We compare the performance of the proposed algorithm with other typical scheduling algorithms and the simulation results show that the algorithm pro- posed outperform other ones in term of QoE and fairness.

Recommendations for Big Data in Online Video Quality of Experience Assessment

Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.

Quality of Experience in Internet of Things: A Systematic Literature Review

With the rapid growth of the Internet of Things paradigm,a tremendous number of applications and services that require minimal or no human involvement have been developed to enhance the quality of everyday life in various domains.In order to ensure that such services provide their functionalities with the expected quality,it is essential tomeasure and evaluate this quality,which can be in some cases a challenging task due to the lack of human intervention and feedback.Recently,the vast majority of the Quality of Experience QoE works mainly address the multimedia services.However,the introduction of Internet of Things IoT has brought a new level of complexity into the field of QoE evaluation.With the emerging of the new IoT technologies such as machine to machine communication and artificial intelligence,there is a crucial demand to utilize additional evaluation metrics alongside the traditional subjective and objective human factors and network quality factors.In this systematic review,a comprehensive survey of the QoE evaluation in IoT is presented.It reviews the existing quality of experience definitions,influencing factors,metrics,and models.The review is concluded by identifying the current gaps in the literature and suggested some future research directions accordingly.

一种基于QoS的QoE到SLA映射方法

该文提出一种算法IQoE2QoS(Improved QoE to QoS),采用模糊理论的方法计算QoE到QoS的映射。该算法有3重目标:从大量的经验数据中通过计算互信息量方式总结被统计指标之间的关联程度。在大量经验数据的基础上通过多指标模糊判定理论将用户感知映射到应用层用户QoS参数。考虑了用户的QoE和QoS的双向映射,并且阐述了得到的QoE如何自然映射到SLA(Service Level Agreement)。通过仿真表明,IQoE2QoS算法对用户体验的分类准确度是线性回归算法的2到3倍。

HTTP移动流媒体QoE管理综述

随着移动通信网络和技术的快速发展,多媒体信息的大量涌现,人们对多媒体信息服务的要求也越来越高.基于HTTP协议的移动流媒体由于拥有广泛运营基础的Web服务器网络环境,部署简单,适用范围广泛等优点成为研究的热点.简要介绍了HTTP移动流媒体的相关特点,重点分析了影响HTTP移动流媒体用户体验质量的各层因素,归纳总结了评价方法和现有的工具,并对HTTP移动流媒体相应的优化进行了介绍,最后对HTTP移动流媒体QoE管理进行了总结和展望.

QoE驱动的VR视频自适应采集与传输

在虚拟现实(VR)视频流媒体传输中,如何在带宽受限的条件下提高用户的质量体验(QoE)是一项巨大的挑战。为了更好地提高资源利用率和用户的QoE,提出了一个面向多用户的QoE驱动上下行链路联合的VR视频流媒体自适应采集与传输系统。与传统的VR视频无线传输系统不同的是,所提系统考虑了上行传输部分。其中,视频服务器根据上行链路和下行链路的带宽信息、用户的实时视角信息,以速率自适应为基础进行码率选择和资源分配。定义了QoE驱动的码率选择和资源分配问题,以最大化整个系统所有用户的QoE值。提出了联合KKT条件和分支定界法的速率自适应选择算法。实验结果表明:所提系统可以有效提高所有用户的QoE值,与上行链路平均分配资源算法相比,系统QoE值提高了14.27%,同时与传统的VR视频速率自适应算法相比,系统QoE值提高了23.47%。

一种基于QoE量化评估的服务选择算法

针对如何为互联网用户从多个相同或相似的服务中进行选择的问题,提出了一种新的服务选择算法:基于Qo E(Quality of Experience)量化评估的服务选择算法(A Service Selecting Algorithm Based on Quantified Qo E Evaluation,ASSABQ).该算法基于一种层次化评分模型,从历史评分中学习获取用户偏好,根据多种评价因素计算每个可用服务的满意度,并选择满意度最高的服务给用户.与已知算法相比,ASSABQ算法的复杂度从O(n2)下降到O(n).仿真实验结果表明,在相同应用场景下,采用ASSABQ算法得到的用户满意度比已知算法提高约10%.

网络传输中IPTV的QoE评估模型的研究

IPTV是一种基于Internet的多媒体网络服务,由于Internet本身的不可靠性,使其在网络传输过程中无法保证服务质量。为了实时定量评估IPTV服务质量,提出了一种基于IPTV的用户体验评估模型,通过建立从网络服务质量QoS到用户体验质量QoE的映射关系,借助QoS测量技术,以实现针对QoE的在线评估。实验建立IPTV仿真平台,模拟真实网络环境下IPTV媒体流传输的整个过程,实现网络损伤QoS可控和QoE可测。针对不同编码和不同内容的视音频,分别建立独立的QoE评估模型。同时考虑到模型对数据精度和计算速度的需求,给出优化的QoE评估模型。实验结果表明,该评估模型与实际用户体验具有较高的拟合度。

基于QOE效用函数的多用户OFDM系统功率分配算法

将用户感受质量(Quality of experience,QOE)引入正交频分复用(Orthogonal frequency division multiplexing,OFDM)系统的资源分配算法设计中,并基于QOE构建的效用函数提出了一种以系统平均QOE最大化为目标的功率分配算法。该算法利用导数迭代逼近的方法调整系统功率分配,从而获得接近最大系统平均QOE的功率分配方法。仿真结果表明,该算法在保证较高系统平均QOE指标的同时,能够依据系统功率资源供求情况,兼顾系统和容量与用户公平性指标。

基于广义线性模型的QoE评估方法研究

基于简化的广义线性模型,建立了QoE等级概率分布回归模型。该模型能够通过网络QoS参数映射得到用户QoE等级累积概率分布。采用最大似然估计完成了对模型参数的估计,并使用皮尔逊方法对模型的拟合优度进行了检验。Web业务的QoE评估实例验证了方法的有效性。

多维吸引子构成模型的视频业务QoE优化

为优化未来多层无线网络覆盖中视频业务的体验质量(quality of experience,QoE),在基于生物信息学的细胞吸引子选择模型上研究多维吸引子选择算法,对每个接入网视频业务的吸引力进行建模,设置参数控制吸引子吸引力度和算法收敛速度,当QoE低于用户容忍阈值时,该模型会根据当前QoE值重新计算各个接入网应分得的视频流量,使当前视频QoE值重新达到用户要求。仿真结果表明,通过持续的反馈-调整闭环机制,使该方法在网络变差时优化视频业务QoE。

移动业务QoE量化指标体系的构建与应用

分析了QoE量化指标的多个属性,提出了移动业务QoE量化指标体系的一种建模方法,并结合现网验证,建立了两种典型移动业务的两套QoE量化指标体系,最后从多个视角剖析了QoE的研究发展方向,为移动网络运营商提升用户感知提供了技术参考。

基于终端测量的移动流媒体QoE评价方法与设计

针对在进行用户体验质量(Quality of Experience QoE)评价时获取数据困难的问题,建立一套在移动终端上获取用户体验数据的方法,来为QoE评价提供数据支持。目前,对于用户体验质量的研究大多都是在实验室环境下进行的,很少考虑到现实环境,导致研究具有局限性。通过设计运行在移动终端的应用程序,可以获取用户在真实环境下的主客观QoE数据,结合Living Lab理论,提出一种基于真实用户环境来研究QoE的方法。实际应用表明,该方法可以较好地评价用户体验质量。

MMS业务的QoE定义与测量

用户体验质量已成为移动通信中的一个热门话题,为了有效地测量多媒体短信业务(MMS)的用户体验质量,分析了QoE的定义以及它与传统QoS的区别,介绍了QoE的KQI和KPI之间的相互关系并建立起QoE的映射模型;然后,结合MMS业务的实现流程以及所涉及的网元设备详细定义了MMS业务中的KQIs与KPIs,最后,针对MMS业务提出了一种基于统计样本的QoE测量方案,通过测量明确KPIs与QoE之间的关系以及影响用户体验质量的因素,这对于提高用户的体验质量有重要意义。

基于QoE的LTE多业务资源分配算法

高效地利用无线频谱资源和保证用户体验质量是未来无线网络的主要目标。基于此,提出一种基于QoE的LTE多业务资源分配算法。在考虑信道信息、QoS要求及公平性的基础上,引入QoE来计算的用户优先级。特别的,引入最小QoE约束来保证RT用户QoE要求;提出一种次优资源块(Resource Block,RB)分配算法来解决复杂的资源分配优化问题,该算法主要分为两步:保证RT用户最小QoE要求;最大化系统加权和速率。仿真结果表明,相较现有的RT/NRT资源分配算法,该算法在用户分组丢失率、平均QoE和小区频谱效率方面性能都有所提升。

面向QoE的电信运营支撑分布式系统的研究与规划

以电信运营支撑分布式系统为研究主体,结合系统涉众的产业链和价值链,分析了其面向体验质量(QoE)需求的重要性,并且根据用户体验过程和用户体验的蜂窝模型划分出了其用户体验层次模型。

E^2-QoE:一种支持QoE的LTE网络传输节能方法

对于流媒体数据业务,用户会更多的考虑其体验质量(QoE).由于LTE网络的利用率较低,在长流传输过程中带宽通常得不到充分利用,造成数据下载时间延长,并可能伴随有网络延时、抖动、数据重传的问题.这种不利因素不仅有损QoE,也造成设备不必要的能耗.针对用户QoE的分析,提出一个基于多层框架的QoE分析模型(M-QoE),它由应用层的QoS机制、网络层的QoE机制和主观QoE机制组成,从多个层面量化分析QoE的影响因素.基于M-QoE模型,提出一种基于QoE的LET传输节能方法,该方法综合考虑了QoE和能效的问题,根据用户行为习惯和感知体验来断定是否终止当前的请求链接,以此增加设备的休眠时间,降低设备的耗能.实验结果表明,E^2-QoE方法具有明显的节能效果.

面向用户QoE的移动IPTV视频质量优化算法

针对现有移动网络环境下IPTV视频质量不能很好满足用户需求的缺点,提出一种面向Qo E的移动IPTV视频质量优化算法。算法设计了移动IPTV的网络体系架构,负责管理视频的传输和执行优化算法,提出移动客户端算法来解决Qo E与Qo S参数在移动客户端与视频优化服务器之间的数据传递问题,并设计视频优化算法来解决最佳IPTV视频服务器的选择问题,最后对应用部署和运行时序进行了分析和阐述。实验结果表明,该模型在移动网络环境下具有较好的网络延迟、抖动和丢包性能,能够较好地满足移动客户端的用户体验需求。提出的优化算法能有效提高移动环境下的视频服务质量。