Impact of video content and transmission impairments on quality of experience

The analysis of the impact of video content and transmission impairments on Quality of Experience (QoE) is a relevant topic for the robust design and adaptation of multimedia infrastructures, services, and applications. The goal of this paper is to study the impact of video content on QoE for different levels of impairments. In more details, this contribution aims at i) the study of the impact of delay, jitter, packet loss, and bandwidth on QoE, ii) the analysis of the impact of video content on QoE, and iii) the evaluation of the relationship between content related parameters (spatial-temporal perceptual information, motion, and data rate) and the QoE for different levels of impairments.     

面向智能终端的数据业务QoE测量体系

为了探究面向智能终端的数据业务QoE测量方法,通过分析QoE研究现状和数据业务用户体验特性,以时延、抖动、信息丢失为依据,建立了针对现有数据业务QoE的立体分类模型。在分析终端对QoE影响的基础上,综合网络、业务、终端三方面影响因素,创新性地提取QoE终端参数影响因子,结合面向业务的网络影响因子提取方法,建立了数据业务的QoE测量体系模型。     

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%。     

服务功能链中基于机器学习的QoE评估与预测

在软件定义网络与网络功能虚拟化协同的网络架构下,只考虑单个服务质量（QoS）指标的服务功能链部署无法满足用户的多业务体验需求。提出一种基于机器学习的服务功能链部署模型。基于层次分析法构造MPNQ2算法以建立QoS与体验质量（QoE）的映射关系,得出影响QoE的网络参数并评估其影响权重。在此基础上,利用具备较强综合学习和泛化能力的随机森林模型对服务功能链的QoE进行预测。实验结果表明,与梯度提升决策树、线性判别分析等机器学习模型相比,随机森林模型为预测QoE的最佳模型,同时在影响QoE的网络参数中,丢包率对服务功能链的部署影响最大。     

Combining traffic-shaping methods with congestion control variants for HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) is a streaming video technique widely used over the Internet. However, it has many drawbacks that degrade its user Quality of Experience (QoE). Our investigation involves several HAS clients competing for bandwidth inside the same home network. Studies have shown that managing the bandwidth between HAS clients using traffic-shaping methods improves the QoE. Additionally, the TCP congestion control algorithm in the HAS server may also impact the QoE, because every congestion control variant has its own method to control the congestion window size. Based on previous work, we describe two traffic-shaping methods, the Hierarchical Token Bucket shaping Method (HTBM) and the Receive Window Tuning Method (RWTM), as well as four popular congestion control variants: NewReno, Vegas, Illinois, and Cubic. In this paper, our objective is to provide a detailed comparative evaluation of combining these four congestion control variants with these two shaping methods. The main result indicates that Illinois with RWTM offers the best QoE without causing congestion. Results were validated through experimentation and objective QoE analytical criteria.     

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.     

A survey of data-driven approach on multimedia QoE evaluation

With the development of mobile communication technology and the growth of mobile device, the requirements for user quality of experience (QoE) become higher and higher. Network operators and content providers are interested in QoE evaluation for improving users’ QoE. However, multimedia QoE evaluation faces severe challenges due to the subjective properties of the QoE. In this paper, we provide a survey of the state of the art about applying data-driven approach on QoE evaluation. Firstly, we describe the way to choose factors influencing QoE. Then we investigate and discuss the strengths and shortcomings of existing machine learning algorithms for modeling and predicting users’ QoE. Finally, we describe our research work on how to evaluate QoE in imbalanced dataset.     

基于用户偏好的网络质量QoE感知建模仿真分析

为了解决现有网络质量QoE感知模型数据粗差迭代次数多、线性回归参数小的问题,提出基于用户偏好的网络质量QoE感知建模仿真研究。依据用户偏好理论确定模型参数,并获取网络质量QoE感知数据,以此为基础,通过MCD算法判别并去除网络质量QoE数据粗差,以去除粗差的网络质量QoE数据为基础,利用ROI加权算法提取网络质量QoE数据特征,以得到的网络质量QoE数据特征为依据,将其代入多元线性回归方程计算网络质量QoE感知,实现了基于用户偏好的网络质量QoE感知。实验结果显示,与现有三种网络质量QoE感知模型相比较,构建的网络质量QoE感知模型降低了数据粗差迭代次数,提高了线性回归参数,充分说明构建的网络质量QoE感知模型具备更好的性能。     

基于QoE感知的中继卫星带宽资源分配方法研究

为改善数据中继卫星的带宽分配对负载传输效率的影响，提出了一种基于用户体验质量QoE感知的中继卫星带宽资源分配方法。考虑到空间信息网络高动态链路情况下传输的可靠性，建立基于多个静止同步轨道卫星协同接入模型，研究了低轨道用户卫星进行多路数据上传的优势特点。通过设置链路效能函数，其将用户QoE满意度量化为链路反馈和链路成本，并用库恩塔克方法求解，实现最大化系统效能和带宽公平分配。研究结果表明，与Load-Balancing和Best-SNR-First方法相比，该方法不仅保证了用户QoE满意度，还提升了网络的接入性能。     

A review of QoE research progress in metaverse

Metaverse is a virtual world that maps and interacts with the real world. It is a digital living space with a new social system created through Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Artificial Intelligence (AI), Cloud Computing (CC) and other technologies. With the rapid development of block chain technology, interactive technology, network and computing technology, communication network technology, digital twin technology, artificial intelligence technology, Internet of Things technology and electronic game technology, the metaverse has also ushered in a stage of rapid development. However, enormous difficulties and challenges remain in how to create a perfect metaverse system that can truly break the barriers between reality and virtual. The quality of content, device and interaction in the metaverse all have an important impact on the Quality of Experience (QoE). The purpose of this paper is to provide the latest research progress of the current metaverse QoE, which will be helpful to the future research of the metaverse QoE and provide the direction for further research.     

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。     

QoE优先的移动网络RRTCC传输改进策略

目前Chrome浏览器为WebRTC适配的RRTCC（receiver-side real-time congestion control)拥塞控制算法在移动网络环境的多流竞争情况下表现较差.在有TCP流共存的情况下会过度退让从而导致WebRTC流饥饿,在视频会议这种多WebRTC流并发的情况下,新加入的WetRTC流会损害已有流的通信质量.针对该问题,提出了QoE优先考量的RRTCC传输控制改进策略MTCIS,该策略根据对当前移动网络性能和QoE需求的分析,采取激进程度适中的WebRTC传输控制方法,避免了与TCP流竞争时过度退让的问题.针对多WebRTC流并发的场景,设计了并发流管理机制,在合理利用带宽和优先满足用户QoE的基础上,对视频流的传输参数进行动态调整.实验结果表明,在移动网络场景多流竞争的情况下,MTCIS策略能够有效地保证WebRTC的合理竞争性、稳定性和健壮性,提高了用户的QoE体验.     

一种基于QoE效用函数的资源分配算法

针对多用户MIMO-OFDM系统,立足业务体验方,给出了一种最大化用户QoE的资源分配算法。通过设计QoE效用函数,将用户QoE与系统QoS参数关联起来,在发送功率和目标误码率的约束条件下,以最大化用户平均QoE为目标,通过QoE效用函数获取用户当前时刻QoE增量,据此确定用户时频资源分配优先级,进而进行注水功率分配。仿真结果表明,该算法能够充分利用系统资源,有效提高用户平均QoE。     

Platform for real-time subjective assessment of interactive multimedia applications

With the advent of cloud computing and remote execution of interactive applications, there is a need for evaluating the Quality of Experience (QoE) and the influence on this QoE of network condition variations, media encoding parameter settings and related optimization algorithms. However, current QoE assessment focuses mainly on audiovisual quality in non-interactive applications, such as video-on-demand services. On the other hand, where experiments aim to quantify interactive quality, the focus is typically targeted at games, using an ad-hoc test setup to assess the impact of network variations on the playing experience. In this paper, we present a novel platform enabling the assessment of a broad range of interactive applications (e.g., thin client remote desktop systems, remotely rendered engineering applications, games). Dynamic reconfiguration of media encoding and decoding is built into the system, to allow dynamic adaptation of the media encoding to the network conditions and the application characteristics. Evaluating the influence of these automatic adaptations is a key asset of our approach. A range of possible use cases is discussed, as well as a performance study of our implementation, showing that the platform we built is capable of highly controllable subjective user assessment. Furthermore, we present results obtained by applying the platform for a subjective evaluation of an interactive multimedia application. Specifically, the influence of visual quality and frame rate on interactive QoE has been assessed for a remotely executed race game.     

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。     

A remote display QoE improvement scheme for interactive applications in low network bandwidth environment

Screen transmission is an essential part of Desktop as a Service (DaaS) which directly influence the quality of experience (QoE). In this paper, we propose a novel QoE improvement scheme that dynamically controls the quality setting of the image compression before the screen transmission to decrease response time of the system still maintaining the satisfactory image quality, hence improves the QoE in interactive applications in a band-limited environment. The proposed scheme first selects the best quality setting appropriate for current network bandwidth quota, then uses the remaining bandwidth to improve the quality setting of low motion regions without any adverse effect on response time. To enable the adaptive quality selection and image quality refinement, we propose a compressed image file size inference model and a block priority calculation method respectively. Particularly, we implement our QoE Improvement Scheme to work with screen content coding. Both quantitative measurements and users’ evaluations in the experiments show that our QoE improvement scheme improves QoS as well as QoE by utilizing the available network bandwidth efficiently.     

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。     

移动视频业务QoE自适应测量工具

针对不同无线环境（3G、WiFi）下获取用户体验质量（Quality of Experience,QoE）数据困难和不精确的问题,提出一种基于安卓（Android）移动终端视频业务QoE的自适应测量方法.通过实时测量并评估用户在线视频业务体验质量,提高用户体验质量评价的准确性和实用性.为此开发了能自动测量视频QoE的工具,测量服务质量（Quality of Service,QoS）客观参数,通过效用函数映射到主观QoE（MOS值）.通过对理论QoE评价模型（取自文献）与用户实际反馈相关性研究改进理论模型.结果表明,无线环境下改进的模型测量结果更接近用户实际反馈,可以更好地评价QoE.     

个人云存储体验质量测量工具的设计与实现

随着用户日益增长的网络存储需求,涌现出了大量个人云存储（PCS）服务平台。个人云存储终端用户使用过程中体验质量（QoE）的测量是终端用户和服务提供商所共同关注的问题。通过从控制流与数据流之间的不同特性方面分析了影响个人云存储体验质量的因素,从终端用户的角度提出了能合理评估个人云存储体验质量的指标,设计了精确测量体验质量评估指标的方法。利用被动测量技术,实现了一个面向终端用户的个人云存储服务体验质量测量工具,同时给出了工具实现中的进程抓包、网络流分类等问题的解决方案。实验结果表明,测量工具运行健壮,测量数据准确,可以用于从用户终端测量个人云存储服务QoE。