Q2ABR: QoE‐aware adaptive video bit rate solution

In this paper, we propose a new adaptive bit rate (ABR) streaming method. This method is based on estimating and monitoring users' video streaming experience, their quality of experience (QoE). This ensures a good user QoE and optimises bandwidth utilisation by monitoring video buffer fill rate to ensure minimal data traffic. First, we achieve a QoE evaluation model based on network bandwidth, video segment representation, and dropped video frame rate parameters. Second, following our QoE evaluation model, we formulate an ABR method using the reinforcement learning (RL) paradigm to select video representations and using a breakpoint detection mechanism to monitor end‐user QoE variation. The proposed ABR method is called “QoE‐aware adaptive bit rate (Q2ABR)” and is composed of three individual modules, one for QoE estimation using machine learning methods, one for QoE variation monitoring using the breakpoint detection mechanism, and one for video representation selection using reinforcement learning. The design objective of Q2ABR is to ensure the overall QoE of these users while maintaining a minimum variation in the standard deviation of the users' QoE values. Third, the performance of the Q2ABR method is evaluated and compared with several existing ABR approaches in the literature using real traces that we collect on different transport scenarios (such as bus and train, among others). Since this method considers the user's perception of video quality as a regulator for optimising the overall video distribution network, good results are ensured in terms of the user's experience and buffer fill rate.     

基于偏好逻辑的网络流QoS类动态聚集方法

现有的网络业务流到QoS(Quality of Service)类的聚集一般采用定量的聚集方式,这类方法需要业务流给出确定的QoS参数值,并且QoS参数之间的权重系数是精确的,系统设置的QoS类也是固定不变的;而现实中,这些因素往往是不确定、不精确的.于是本文引入定性的偏好逻辑理论、并结合QoE(Quality of Experience)建模业务流的偏好需求,再基于霍尔逻辑对冲突的偏好需求进行有效的检测和消除,继而借助非单调推理在动态变化的候选集QoS类中进行选择,最终实现一种以偏好为内容的QoS类动态聚集方法 PLM(Preference Logic Model for flows aggregation).实验结果表明,本文提出的聚集方法,可有效建模业务流不确定、不精确的QoS需求;在高可变的动态环境中,当业务流QoS需求发生变化,或QoS类发生变化,都能对业务流进行有效的聚集调节以充分利用系统资源.因此,与其他聚集方法相比,在延时、丢包率、吞吐量等各个方面表现优良.     

基于状态机的视频码率自适应算法

由于链路带宽存在随机性,已有的基于超文本传输协议的动态自适应流媒体传输技术（DASH）的码率自适应算法不能很好解决播放流畅性和视频质量之间的矛盾。为解决该问题,提出一种基于状态机的DASH（SDASH）算法,将码率切换过程用状态机进行分析与控制。首先充分考虑客户端观看体验质量（QoE）的影响因素,对影响因素进行数值分析,并设定6个码率等级状态;然后将视频码率与影响因素的数值变化之间的联系作为状态转移条件;最后在保证播放缓存和码率偏移率处于一定阈值的条件下将视频码率切换至视频质量和播放流畅性整体性能相对最佳的码率等级上。实验结果表明,该算法与基于模糊逻辑控制的码率自适应算法相比能够提高客户端请求视频的平均码率,且尽量避免出现码率骤降等情况,从而较好地平衡播放流畅性和视频质量之间的关系,提升了视频观看过程的体验质量。     

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。     

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。     

Quality of experience measurement for light field 3D displays on multilayer LCDs

Multi‐layer light field displays (MLLFDs) are a promising computational display type that can not only display hologram‐like 3D content but will also be well compatible with normal 2D applications. However, the quality of experience measurement for MLLFDs is always an important yet challenging issue. Despite existing research works on MLLFDs, most of them only provide quality of experience results with qualitative evaluation, for example, software simulation of a few 3D images/videos, rather than rigorous quantitative evaluation. This work targets at building a unified software and hardware measurement platform for different MLLFD methods, and comprehensively measuring both objective and subjective performance based on virtual object models. To the best of our knowledge, it is the first time that such performance has been measured for MLLFDs. In addition, to use the existing disclosed virtual object sequences, this paper further proposes three customized virtual models, which are the USAF‐E model, the view angle model, and the concave/convex object model for accurate measurement of spatial resolution, viewing angle, and depth resolution. A toolbox for MLLFD measurement with proposed models is also released in this paper. The experimental results demonstrate that our proposed measurement method, models, and toolbox can well measure MLLFDs in different configurations.     

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.     

NPS用户群网络感知保障运营体系研究

介绍当前用户网络感知问题及解决手段,提出一种提升NPS(净推荐值)用户群网络感知的综合解决方案。创新建立一种客户感知Qo E(体验质量)得分模型,从用户控制面与业务面建立用户对网络感知的得分体系。同时提出一种NPS识别模型,实现NPS网络感知闭环管理流程,通过挖掘Qo E得分低的NPS质差用户,聚类用户共性问题。针对指标劣化产生的告警,深入分析原因并协助维护人员和厂家快速定界定位问题,提升客户网络感知。     

MIMO Scheduling Effectiveness Analysis for Bursty Data Service from View of QoE

In the user selection phrase of the conventional Multiple-input-multiple-output (MIMO) scheduling schemes,the frequent user exchange deteriorates the Quality of user experience (QoE) of the bursty data service.And the channel vector orthogonalization computation results in a high time cost.To address these problems,we propose an inertial scheduling policy to reduce the number of noneffective user exchange,and substitute self-organization policy for channel vector orthogonalization computation to reduce computational complexity.The relationship between the scheduling effectiveness and the inertia of objective function is observed in the simulation.The simulation results show that the inertial scheduling policy effectively reduce the number of potential noneffective scheduling which is inversely proportional to the Mean opinion score (MOS) that quantifies the QoE.Our proposed scheduling scheme provides significant improvement in QoE performance in the simulation.Although the proposed scheduling scheme does not consider the channel vector orthogonalization in the user selection phrase,its throughput approaches the level of the throughput-oriented scheme because of its selforganization scheduling policy.     

TD-LTE中基于QoE的资源调度机制研究

提出了一种新的基于QoE的网络资源调度机制,用以保障用户体验质量和提高网络资源利用效率。利用基于业务的关键性能指标将用户体验质量进行量化,从而建立关键体验指标体系。并且设计出对用户体验质量进行监控和管理的框架,用以进行网络资源调度,为运营商实现高效合理的资源分配提供了一种新的思路。