IP网络中视频流QoE评价模型

网络视频业务的兴起使网络运营商和服务提供商更加关注视频的用户体验（QoE）,然而视频用户体验（QoE）值由于其主观性且评价过程复杂,难以在视频流传输中实时获取。通过实验分析了视频传输过程中服务质量（QoS）参数变化对视频QoE的影响,建立了客观、可测量的QoS参数与视频QoE之间映射模型,用可量化的QoS参数来评定视频QoE受网络性能的影响程度,以评估网络视频质量,该模型形式简单,能够实时监测视频质量。实验结果表明,该模型的评价结果能较好反映视频QoE。     

3G网络中移动视频质量评估模型的研究

随着3G网络技术的不断发展和广泛应用,移动视频业务比以往更受用户的关注。与传统的有线网络视频业务相比,移动视频的传输条件不太稳定,更容易产生误码;移动终端的视频播放性能更容易受到设备硬件的限制,这就要求有更适合移动终端的视频编码方式。此外,不同类型的视频内容、用户的兴趣爱好等因素也会对用户观看视频的体验产生不同的影响。以上因素给移动视频服务提供商在业务质量的评估以及用户体验的提升方面提出了巨大的挑战。目前在移动视频质量评估的研究中,主要采用基于服务质量(Quality of Service,QoS)的评价方法,但是这些方法没有考虑用户主观体验参与在内的诸多因素,因此并不是一种非常有效的评价方法。针对影响移动视频用户体验质量的主客观因素,研究了无线参数、终端设备参数和视频编码参数对移动视频质量的影响,提出了基于用户体验质量(Quality of Experience,QoE)的视频质量评价方法。     

基于用户QoE的流媒体系统性能测评模型设计

影响流媒体用户服务质量体验QoE(Quality of Experience)的因素有很多,如何对用户QoE进行量化判别是一个复杂的问题。为此,以媒体传输指标MDI为基础,研究了用于测量流媒体用户QoE的基本指标,并提出了相关测量数据的获取方法。以流媒体用户的实际体验质量为依据,提出了一种有效的流媒体系统性能评价模型,该模型为流媒体系统的性能分析和优化调整提供了理论基础,具有广泛的应用价值。     

移动流媒体用户QoE评估模型

HAS(HTTP Adaptive Streaming)能够实现流畅播放和视频质量的平衡,为用户提供更好的服务质量体验。大多数基于HAS的流媒体用户体验质量(Quality of Experience,QoE)模型考虑了当前系统或网络条件,但对用户所处环境的客观影响、用户心理因素的考虑较少。面向移动流媒体客户端的应用场景,从客观感知影响参数和心理效应影响参数两个方面来考虑移动端流媒体的QoE影响因素,设计用户QoE评估模型。提出移动设备抖动状态检测和用户观看位置检测方法,并将设备抖动状态、用户观看位置与流媒体服务质量相结合,再根据心理学系列位置效应来综合评估用户的质量体验情况。最后通过实验证明了所提的用户QoE模型能够提供准确有效且符合用户实际体验的QoE评估结果。     

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。     

面向体验质量的多媒体计算通信

随着移动互联网和通讯技术的发展,多媒体通信技术成为国家信息产业发展的重大需求,广泛地应用在视频会议、各类直播应用、远程医疗、远程监控和远程教育等方面。然而,大容量多媒体通信业务面临着网络带宽的压力。本文将媒体计算引入通信系统,建立新的多媒体通信研究范式,从提升体验质量（quality of experience,QoE）的角度,形成新的多媒体编码与传输方法,从根本上降低网络带宽需求的压力。体验质量即信息接收者结合自身期望对客观信息载体的有关性能给出的主观评价,是区别于服务质量（quality of service,QoS）的通信质量评价准则。本文介绍了QoE的评价准则,分为基于用户的评价方法和基于客观参数的评价方法,通过用户主观评分或对用户的相关生理、心理指标进行测量进而分析、推测用户的感受;或者通过对业务客观指标的主观化修正实现体验质量的评价。本文综述了多媒体编码方法,主要包括基于波形的编码和基于内容的编码方法。前者对任意视频信号进行有效编码而不需要分析视频内容,如一系列视频编码标准;后者识别视频序列中的物体和相关区域并对它们进行编码。本文阐述了5G+AI（artificial intelligence）时代的新型视频传输方法,如多视点视频编码、4 K、8 K视频编码,3D立体视频,点云、光场、AR（augmented reality）、VR（virtual reality）等视频业务。     

基于QoS量化用户体验质量的评价模型

针对网络环境中业务供应商提供的业务不能很好的满足用户需求的问题,引入用户体验质量（QoE）并结合服务质量（QoS）参数,通过仿真网络得到相应数据,使用matlab工具分别建立用户体验质量与比特率、用户体验质量与丢包率的评价模型.运用统计分析方法对评价模型进行数据分析,并与史蒂文斯幂定律、韦伯-费希纳定律等模型进行对比验证.结果表明,构建的评价模型能更精确的体现用户体验质量与服务质量的关系,为供应商提供了重要的参考依据,并指明了服务方向.     

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.     

基于众包感知的OTT业务体验影响因素分析

随着移动互联网和OTT （Over-the-top）业务的高速发展,传统以网络为中心的运维方式难以为继,因此如何提高用户业务体验、变革传统网络质量的评价和优化方法迫在眉睫。本文围绕基于终端侧业务感知大数据进行网络和业务质量评价这一新型网络评价与运维作业模式,首先对影响端到端用户业务感知的因素进行了较全面的剖析;并利用从普通用户终端上采集的现网真实的海量业务感知数据,重点针对网页浏览这一代表性的OTT业务,从多个不同的维度深入研究影响业务感知的关键因素,以及关键业务感知指标间的关联关系,揭示了OTT业务感知的关键影响因素间的内在联系。研究结果对于进一步分析业务感知质差成因、合理构建用户体验质量（Quality of experience,QoE）映射模型等具有很好的参考价值。     

基于XDR数据分析的OTT视频服务感知质量评估方法

互联网电视(over the top, OTT)视频业务逐渐成为最流行的在线业务之一,然而网络视频往往由于网络质量差、服务平台过载等原因,出现播放失败、卡顿次数增加、缓冲时间过长等质量问题,导致用户感知质量(quality of experience, QoE)下降.因此,运营商需要精确评估和掌握用户在使用网络视频业务过程中的质量体验,以便提前发现质量问题,进一步开展网络和业务优化工作.为了解决该问题,提出一种基于用户呼叫/事务/会话记录数据(extend data record, XDR)的无参考网络视频质量评估方法.该方法从大量XDR数据中提取出与视频质量相关性高的少量信息,将大规模、低价值的XDR话单数据转化为高价值、小规模的视频质量特征信息,有利于后续人工智能算法的应用和视频业务质量评价,降低进一步数据挖掘的资源成本,提升机器学习的输入样本质量和QoE评价结果的准确性.实验表明：使用该方法提取后的数据进行QoE预测,得到的预测结果在准确性方面明显优于目前基于原始XDR数据的QoE机器学习评估方法.     

一种视频质量监测系统的设计与实现

网络服务提供商希望能从用户的角度了解目前网络所提供的服务质量,而用户也希望获得定量的指标来评价当前网络服务质量。为此,以视频质量监测为研究对象,提出一种面向用户体验质量的网络监测系统。通过实验分析了网络传输过程中QoS参数对视频QoE的影响;提出一种将视频流转化为测试序列的视频丢包测量方法,该方法能低入侵、准确测量视频传输过程中的丢包情况;基于以上的研究成果,通过对MIB库的扩展和对MIB库轮询机制的研究,构建了面向QoE的视频服务监测系统,该监测系统结构简单、可行性强,实验表明可实时对网络中的视频服务质量进行监测。     

QoE assessment model for multimedia streaming services using QoS parameters

This paper proposes a video QoE (Quality of Experience) assessment model which can assess video quality of experience with only QoS (Quality of Service) parameters and their relative importance at network layer. Since network or service providers can forecast whether to provide multimedia services above a certain level of service quality using the proposed model, they can offer and maintain optimum network environment for multimedia service such as IPTV. Through an experiment of video quality comparison we show that our QoS/QoE correlation model is closely related with video quality degradation patterns to network environmental change.     

Quality enhancement for scalable video delivery over wireless networks based on particle swarm optimization

In this paper, the main objective is to find an optimal rate allocation strategy that can maximize the total-weighted quality of experience (QoE) associated with multiple video sources over error-prone multi-hop wireless networks based on the particle swarm optimization (PSO) technique which belongs to the family of swarm intelligence algorithms. In video transmission over such wireless networks, many network-based (packet loss, delay etc.) and source-based (encoding quantization level etc.) parameters can impair the perceived video quality. The main contributions of the proposed work are twofold. At first, an optimal bandwidth allocation framework is being developed based on PSO in which by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized. Second, these optimal rates have been used for differentiated QoE enforcement between multiple competing scalable video sources. The resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of a moderate scalable video encoder such as H.264/MPEG4 AVC. The aforementioned weight parameters are selected based on the importance of each video sequence’s quality and can be associated with some previous service level agreement-based prices. A strong motivation for differentiated quality enforcement is that video sources need to be encoded differently for different resolutions to cater to diverse devices from mobile displays to HDTV displays. Some numerical analysis have been presented to validate the theoretical results and to verify the claims.     

Impact of packet loss on the perceived quality of UDP-based multimedia streaming: a study of user quality of experience in real-life environments

Multimedia content delivery has become one of the pillar services of modern day mobile and fixed networks. The variety of devices, platforms, and content providers together with increasing network capacity has impacted the popularity of this type of service. Considering this context, it is crucial to ensure end-to-end service quality that can fulfill users’ expectations. The user quality of experience (QoE) for multimedia streaming is tempered by numerous objective and subjective parameters; therefore, it is important to understand the relationships among them. In this paper, we thoroughly examine the impact of packet loss on user QoE in cases when multimedia streaming service is based on underlying User Datagram Protocol. The dependencies between the chosen objective and subjective parameters and the user QoE were examined in a real-life environment by conducting a survey with 602 test subjects who rated the quality of a 1-h documentary film (72 different test sequences were prepared for the rating process). Based on the obtained results, we ranked the objective parameters by their order of importance in relation to their impact on user QoE as follows: (1) total duration of packet loss occurrences (PLOs), i.e., quality distortions in a video; (2) number of PLOs; (3) packet loss rate; and (4) duration of a single PLO. We also demonstrated how the overall user experience can be redeemed, despite the perceived quality distortions, if the content is entertaining to the viewer. The user experience was also found to be influenced by the existence/non-existence of video subtitles.     

QoE-Aware wireless video communications for emotion-aware intelligent systems: A multi-layered collaboration approach

With the ever increasing demand on high-quality visual information for emotion-aware intelligent systems, wireless video traffic explosively grows and causes great energy consumption. Therefore, providing high quality of experience (QoE) for connected users becomes increasingly important. Aiming to establish a new paradigm to solve this challenging problem, in this article we propose a multi-layered collaboration approach to provide energy-efficient QoE-aware wireless video communications by efficiently utilizing the limited transmission resources of wireless networks for 5G. We first investigate the emotion-aware intelligent system QoE measurement based on objective metrics of quality of service (QoS). Then, we utilize the multi-layered collaborations of physical, network and application layers among the connected users to achieve energy-efficient QoE-aware video communications. By developing a profound understanding of the interplay between the video applications and wireless networks, we qualitatively analyze how QoE can benefit from the multi-layered collaborations, and quantitatively assess the achievable gains in a typical wireless-connected emotion-aware application scenario.     

Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change

Due to the variability of wireless channel state, video quality monitoring became very important for guaranteeing users’ Quality of Experience (QoE). QoE presents the overall perceptual quality of service from the subjective users’ perspective. However, because of diverse characteristics of video content, Human Visual System (HVS) cannot give the same attention to whole scene simultaneously when facing video sequence. In this paper, we proposed a video quality assessment model by considering the influence of fast motion and scene change. The motion change contribution factor and scene change contribution factor are defined to quantify the characteristics of video content, which is closely related to the users’ QoE. Based on G.1070, our proposed model considers the influential factors of loss nature of video coding, variability of practical network and video features. Also, the proposed model owns low computational complexity due to the compressed domain approach for the estimation of the model parameters. Therefore, the video quality is assessed without fully decoding the video stream. The performance of our proposed model has been compared with five existing models and the results also shown that our model has high prediction accuracy closing to human perception.     

User feature-aware trustworthiness measurement of cloud services via evidence synthesis for potential users

Cloud computing can provide elastic and dynamic resources on demand, which facilitates service providers to make profits resulting from the long tail effect. It becomes vitally important to ensure that cloud services can be acceptable to more potential users. However, it is challenging for potential users to discover the trustworthy cloud services due to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. This paper presents a user feature-aware trustworthiness measurement approach for potential users. In this approach, the influence factors of QoE are systematically analyzed based on the user feature model and the quantitative computation methods are designed to measure the user feature similarity. In addition, employing FAHP (fuzzy analytic hierarchy process) method identifies the user feature community. To enhance the accuracy of trustworthiness measurement, the false evidences in QoE evaluations are iteratively filtered out with dynamic mean distance threshold. Finally, the trustworthiness of service is measured via evidence synthesis combining user feature similarity. The experiments show that this approach is effective to improve the quality of trustworthiness measurement, which is helpful to solve information overload problem and cold start problem of trusted service recommendation for potential users.     

一种基于视频分类的码率自适应算法

流媒体的码率自适应算法依据网络状态动态调节视频块的码率,提升用户体验质量,但忽略了视频类型的差异对用户体验质量的影响,导致算法性能下降。提出区分视频类型特征的码率选择算法C-ABR。设计相应的用户体验质量效用函数,使用强化学习算法训练模型A3C,提升用户体验质量。实验结果说明,相对于典型的码率自适应算法Pensieve和MPC,C-ABR算法用户体验质量分别提升22.7%和50.4%。     

基于双向强化学习与动态码率调节的无线mesh网络协议

针对现有无线mesh网络协议的用户体验质量(QoE)较差的问题,提出一种基于双向强化学习与动态码率调节的无线mesh网络协议。首先,设计了兼容不同服务类型的无线mesh网络QoE度量框架;然后,设计了基于双向强化学习的无线mesh网络路由协议;最终,结合QoE感知的差异化报文调度策略与数据流源节点码率动态调节算法进一步优化终端用户的QoE质量。基于NS-2仿真平台的对比实验结果显示,本协议可明显地提高无线mesh网络的QoE指标,同时具有较低的控制开销。     

Bandwidth allocation for video transmission with differentiated quality of experience over wireless networks

Delivering digital video content with enhanced quality of experience to the end users over error-prone multi-hop wireless networks is a challenging issue. In video transmission over such wireless networks, many network-based (packet loss, delay, etc.) and source-based (encoding quantization level etc.) parameters can impose some levels of impairment on the perceived video quality. In a video quality enhancement strategy, accurate video quality metrics play a crucial role in the designing process of optimal rate (bandwidth) allocation algorithms. Many cross-layer optimization (CLO) based rate allocation strategies have been developed for this purpose which consider different objective functions (congestion level, total packet loss, etc.) in wireless networks. The main contributions of the proposed work are twofold. At first, an optimal bandwidth allocation framework is being developed in which based on some network-specific constraints and by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized bases on cross-layer design techniques. Secondly, these optimal rates have been used for differentiated Quality of Experience (QoE) enforcement between multiple competing video sources. The resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of a moderate video encoder such as MPEG4. The aforementioned weight parameters are selected based on the importance of each video sequence’s quality and can be associated with some previous service level agreement (SLA) based prices. Some numerical analysis have been presented to validate the theoretical results and to verify the claims.