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 management for video conferencing applications

We introduce a framework for managing the QoE of videos coded with the H.264 codec and transmitted by video conferencing applications through limited bandwidth networks. We focus our study on the medium-motion videos with QCIF, CIF, and VGA resolutions, the most pervasive video formats used by video conferencing applications across the Internet and cellular telephony systems. Using subjective tests for measuring the level of video quality perceived by end users, we expose the relation between the main influential video parameters and the quality experienced by end users. Furthermore, after investigating the effect of different frame rates and compression levels on video streaming bit rate, and consequently on QoE, we propose a QoE control mechanism for limited-bandwidth situations. A congestion control technique is also introduced in this paper and used in simulations for verifying the efficiency of the proposed QoE management algorithm and to implement this algorithm for practical applications.     

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%。     

Quality assessment for a visual and automatic license plate recognition

Video transmission and analysis is often utilized in applications outside of the entertainment sector, and generally speaking this class of video is used to perform specific tasks. Examples of these applications include security and public safety. The Quality of Experience (QoE) concept for video content used for entertainment differs significantly from the QoE of surveillance video used for recognition tasks. This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality. Recognizing the growing importance of video in delivering a range of public safety services, we focused on developing critical quality thresholds in license plate recognition tasks based on videos streamed in constrained networking conditions. Since the number of surveillance cameras is still growing it is obvious that automatic systems will be used to do the tasks. Therefore, the presented research includes also analysis of automatic recognition algorithms.     

Not All Packets Are Equal, Part I: Streaming Video Coding and SLA Requirements

In this first part of a two-part article, the authors consider the network factors that impact the viewers' quality of experience (QoE) for IP-based video-streaming services such as IPTV. They describe the IP service-level requirements for a transported video service and explain MPEG encoding to help readers better understand the impact that packet loss has on viewers' QoE.     

Content driven QoE assessment for video frame rate and frame resolution reduction

Video bit rate reduction is very important for all video streaming applications. One possibility involves quantization domain and the majority of the work devoted to bit rate reduction focuses on this aspect only. The other possibility is to modify a video in time or space domain i.e. change the frames per second FPS rate or frame resolution FR. In this paper we present two no reference metrics mapping FPS rate and FR into MOS (Mean Opinion Scale). The performance of both models is significantly improved by incorporating content characteristics such as spatial information SI and temporal information TI. The impact on Quality of Experience (QoE) of both content characteristics is discussed with relation to the FPS rate and FR changes and general conclusions are drawn. The models were estimated and verified upon results of subjective experiments performed using video sequences of diverse spatial and temporal variability. The considered FPS rate was changed from 5 to 30 and the considered FR was changed from SQCIF to SD.     

Learning from users: a data-driven method of QoE evaluation for Internet video

Multimedia Tools and Applications - Improving quality of experience (QoE) is increasingly significant for Internet video content providers. The essential issue is how to evaluate QoE under the...     

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.     

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.     

Direct push–pull or assisted push–pull? Toward optimal video content delivery using shared storage-based cloud CDN (SS-CCDN)

Video content delivery networks face many challenges such as scalability, quality of service and flexibility. Video suppliers address them through CDN. Cloud computing and Video content Delivery as a Service (VDaaS) plays a key role in improving the content delivery standard and makes the work of content providers, easier. By hosting video contents in the cloud, the content delivery costs are minimized and the overall content delivery performance enhanced by optimization of cloud CDN. Cost optimization of the cloud-based content delivery network requires a focus on delay or throughput, the overall performance and content delivery. The content placement and content access, the QoS and the QoE in CDN can be improved by enhancing the video content delivery performance. In this paper, a unique model for video content delivery, cloud-based is developed, titled as shared storage-based cloud CDN (SS-CCDN) to achieve the objective. This design optimizes through algorithms, the effective placement of video data and dynamic update of video data. For analysis, GA, PSO, and ACO algorithms are used. The proposed model uses direct and assisted push–pull content delivery schemes for cost-efficient content delivery. The low-cost VDaaS model reduces the storage cost, keeps the latency and the traffic cost. Experimental results validate that this model, with regard to storage, traffic, and latency generate higher performance with lower price and satisfy the QoS and QoE aspects in content delivery.

     

HAS QoE prediction based on dynamic video features with data mining in LTE network

Evaluation of HTTP adaptive streaming (HAS) quality of experience (QoE) over LTE network is a challenging topic because of multi-segment and multi-rate features of dynamic video sequences. Different from the traditional QoE evaluation methods based on network parameters, this paper proposes the HAS QoE prediction methods based on its dynamic video segment features with data mining. Considering the application requirement of the trade-off between accuracy and complexity, two sets of methodologies are designed to evaluate the HAS QoE including regression and classification. In regression method, we propose the evolved PSNR (ePSNR) model using differential peak signal to noise ratio (dPSNR) statistics as the segment features to evaluate HAS QoE. In classification method, we propose the improved weighted k-nearest neighbors (WkNN) by using dynamic weighted mapping according to the position of video chunk to meet the dynamic segment and rate features of HAS. In order to train and test these methods, we build a real-time HAS video-on-demand (VOD) system in LTE network and do subjective test in different video scenes. With the mean opinion score (MOS), the regression and classification methods are trained to predict the HAS QoE. The validated results show that the proposed ePSNR and WkNN methods outperform other evaluation methods.     

Subjective evaluation of critical success factors for a QoE aware adaptive system

For many years video content delivery has established itself as the killer application. Improving QoE on adaptive streaming is focusing many efforts in the quest for optimized methods and metrics to allow a QoE driven adaptation. Questions such as whether adaptive systems based on Scalable Video Coding improve subjective quality and in which situations or to what degree are still open issues. Tolerance and indifference thresholds for each type of content, conditions or viewer category, with regard to adaptive systems are critical success factors that are yet unresolved. We compare the performance of a complete adaptive system with the traditional, i.e. non-adaptive, approach in subjective terms. Results of surveying 75 participants show that the adaptation improves QoE under most of the evaluated conditions. Tolerance thresholds for triggering adaptation events have been identified. Users accustomed to Internet video are more critical than users that only watch TV. The under 35 year old subset among the available population is generally more satisfied with the adaptive system than the older subset.     

Adaptive IPTV services based on a novel IP Multimedia Subsystem

Heterogeneous communication devices are emerging and changing the way of communication. Innovative multimedia applications are now accessible through these embedded systems. The 3GPP IP Multimedia Subsystem (IMS) provides a basic architecture framework for the Next Generation Network (NGN) supporting the convergence platform for service provisioning in heterogeneous networks. ETSI TISPAN standardization effort focuses on delivering IPTV services on such platform. Nevertheless, IPTV on IMS standardization suffers from a lack of efficient user-centric network management mechanisms as the end-user may consume IPTV service from different access networks, on different mobile devices, at anytime. User’s Perceived Quality of Service (PQoS) or Quality of Experience (QoE) of IPTV service may also suffer from wireless access network impairments. This paper introduces new functionalities in IPTV over IMS architecture which optimize satisfaction of the end-user and resource utilization of the operator’s networks. A context-sensitive User Profile (UP) model is used to deliver IPTV streams adapted to the user’s environment. In order to optimize the operator network usage, the impact of spatiotemporal dynamics of the video content on the deduced perceptual quality is considered. A Multimedia Content Management System (MCMS) is proposed to perform dynamic cross-layer adaptation of the IPTV stream based on PQoS measurements at the end-user side.     

运动感知基于缓存的自适应视频流传输

目的 基于缓存的自适应视频流传输策略无需估测实时带宽,直接通过缓存变化量与码率的映射函数选取符合当前网络状况的最佳质量码流传输。传统基于缓存的自适应视频传输不考虑内容特征,在码率选择上为不同运动级别视频内容均使用相同的码率映射函数,在不稳定的无线网络环境中高运动强度内容的码率急剧降低会严重伤害用户体验质量（QoE）,提出运动感知基于缓存的自适应视频流传输（MA-BBA）算法。方法 MA-BBA算法根据片段运动级别确定码率映射函数,对运动强度高的内容快速切换到较高码率,而对于运动强度较低的内容则使用较为保守的码率,从而使得缓存资源能够位于安全边界之上且较多分配给高级别运动内容,提高不同运动强度内容的平均质量,使整体QoE得到优化。结果 在公开的无线网络带宽数据集上实现本文MA-BBA算法,基于吞吐量的自适应传输算法（TBA）和基于缓存的自适应传输算法（BBA）。MA-BBA在高运动强度内容的平均质量上比TBA和BBA分别提高1.7%和1.2%,且质量波动区间更小。MA-BBA在平均缓存利用率上达到72%,大大高于TBA的45.9%和BBA的45.4%。结论 MA-BBA算法与现有的码率自适应算法TBA和BBA相比,大大提高了缓存资源利用率,提高了对资源要求最苛刻的高级别运动内容的传输质量,减小码率切换幅度频率,优化了视频服务的整体QoE。     

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.     

Joint In-network Video Rate Adaptation and Measurement-Based Admission Control: Algorithm Design and Evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other’s performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider’s objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider’s policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario.     

On the modelling of CDNaaS deployment

With the increasing demand for over the top media content, understanding user perception and Quality of Experience (QoE) estimation have become a major business necessity for service providers. Online video broadcasting is a multifaceted procedure and calculation of performance for the components that build up a streaming platform requires an overall understanding of the Content Delivery Network as a service (CDNaaS) concept. Therefore, to evaluate delivery quality and predicting user perception while considering NFV (Network Function Virtualization) and limited cloud resources, a relationship between these concepts is required. In this paper, a generalized mathematical model to calculate the success rate of different tiers of online video delivery system is presented. Furthermore, an algorithm that indicates the correct moment to switch between CDNs is provided to improve throughput efficiency while maintaining QoE and keeping the cloud hosting costs as lowest possible.

     

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。     

移动视频体验质量的量化评价模型

移动视频体验质量（QoE）的研究正变得日益重要,现有的QoE评价模型缺少相应的量化方法。结合移动视频业务的特点,提出了两层权值的QoE量化评价模型,即QoE由六种“影响层面”按各自的一级权值叠加而成;进一步地,每个“影响层面”又由多个“影响因素”构成。除了丢包和抖动两个“影响因素”外,其他“影响因素”均具有独立的二级权值。提出通过层次分析法（AHP）计算出所有的一级权值,并且进一步计算出除了丢包、抖动外的其他二级权值。由于丢包和抖动这两个影响因素相互依赖,无法计算各自的二级权值,因此通过网络模拟实验,找出两者的双变量函数f（丢包,抖动）,由此得到完整的两层权值QoE量化评价模型。所提QoE量化评价模型通过测评人员的QoE评分进行验证,实验结果表明该模型获得了较高的准确度,证明了上述模型量化方法的可靠性。