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.

QoE-driven voice quality evaluation model of VoIP based on network simulation

The quality of experience（ QoE） evaluation model for voice over IP（ VoI P） service is studied to analyze the impact of network parameters on voice quality and monitor voice quality in real-time for operators.Firstly,the influence of some network parameters on the voice quality of VoI P is investigated. Then,a simulation platform for VoI P transmission is built to collect voice data under different network enviornments. According to the simulation results,a new mapping model between these arguments and VoI P voice quality is deduced. Finally,the accuracy of this voice quality evaluation model is examined and the results demanstrate that it has high reliability and feasibility.

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.

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。

3GPP R17 NR QoE标准进展及演进技术研究

5G新业务在用户体验方面提出新的要求,针对5G用户业务体验的保障、优化及提升成为业界重点关注的问题之一。3GPP R17版本开展支持5G用户业务体验端到端保障机制的标准研究与制定工作,研究并制定支持5G新业务的用户体验数据采集、优化等系列标准流程,并将在R18版本进一步增强用户业务体验的技术方案,更好地支持5G XR等新业务的高品质服务保障的需求。对3GPP R17和R18用户业务体验无线网技术解决方案进行分析研究,并结合5G新业务需求,展望该方案在增强5G网络高质量业务体验方面的广阔应用场景。

高QoE的低时延智能网络数据传输调度算法

面向低时延、稳定传输、高用户体验质量(quality of experience,QoE)的网络实时传输需求场景,提出一种低时延智能网络数据传输调度算法。该算法由数据块排队控制策略和拥塞控制策略两部分组成。数据排队控制策略提出了综合数据块的创建时间和有效时限(effective time)的性价比模型,有效地解决了传输时间约束下的信息传输不均衡问题;拥塞控制策略提出了基于使用耿贝尔分布(Gumbel distribution)采样重参数化与混合经验优先级模型改进后的深度确定性策略梯度(deep deterministic policy gradient,DDPG)方法,解决了深度确定性策略梯度不适用于离散网络动作空间拥塞控制的问题,并通过学习自适应调整发送参数显著提升了网络拥塞控制质量。实验结果表明,实时传输场景下使用本文提出的排队算法能够有效提升QoE,采用改进后的DDPG进行拥塞控制能大幅降低传输时延。同样场景下,将提出的智能网络数据传输调度算法与排队策略及拥塞控制策略相结合,与传统的网络数据传输调度算法相比,能够更好地兼顾低时延和稳定传输,提供更高的数据传输质量。

Novel E2E-QoE Metric for PHY Optimization:A Cross-Layered Framework

Existing systems use key performance indicators(KPIs)as metrics for physical layer(PHY)optimization,which suffers from the problem of overoptimization,because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste.Therefore,it is necessary to utilize directly the quality of experience(QoE)of user as a metric of optimization,which can achieve the global optimum of QoE under cost and energy constraints.However,QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY.To address this problem,we in this paper propose a novel end-to-end QoE(E2E-QoE)based optimization architecture at the user-side for the first time.Specifically,a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE.Based on this,an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies.Finally,we investigate to optimize the PHY algorithm directly based on the E2E-QoE.The proposed frameworks and algorithms are all validated using the data from real fifth-generation(5G)mobile system,which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.

全景视频基于块的视口自适应传输方案综述

全景视频由于独特的沉浸式、交互式体验受到广泛关注。全景视频传输所需的高带宽、低时延给现有网络传输系统带来了挑战。基于tile(块)的视口自适应传输可以有效缓解全景视频所带来的传输压力,成为当前的主流方案和研究热点。通过分析基于tile的视口自适应传输方案的研究现状和发展趋势,对该传输方案的两个重要模块,即视口预测与码率分配进行论述,从不同视角归纳总结相关领域的方法。首先,基于全景视频传输框架对相关技术进行阐明;其次,从主、客观两个维度分别介绍评估传输系统性能的用户体验质量的指标;再后,分别从视口预测、码率分配两方面进行归纳,系统梳理经典的研究方法;最后,基于当前研究现状讨论全景视频传输的未来发展趋势。

基于Kano模型的列车正性情感体验需求研究

目的遵照“以事实为依据”的设计原则,明确对列车样本展开设计分析的内容与标准,获得准确的体验需求。方法首先基于哈森扎尔在情感体验方面的研究,提出正性品质理论模型及其需求洞察路径,用于分析、提取列车乘客的情感体验需求;其次利用Kano模型对需求进行优先级排序;最后以享誉世界的儿童列车“阿苏男孩号”对方法进行了应用验证。结果获得了22项需求及其优先级排序,总结得出儿童列车体验设计的策略:空间设计优先考虑提供就餐场所,以及针对用户分层做空间分割;设施设计优先考虑亲子互动性;涂装设计优先考虑“列车外壳”;服务设计优先考虑为家长陪伴提供条件。结论该方法可以帮助设计师洞察样本列车的体验需求及其优先级排序,能够为优化列车自身体验与指导同类列车设计提供决策支持。

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.

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%.

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%。

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。

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。