基于模糊数学理论的流媒体业务质量评估方法

本文在综合全面地考虑了影响用户视觉感受的多种因素的基础上,结合近年对流媒体业务测试的经验数据,建立了流媒体业务质量评测的数学模型,该模型在引入模糊数学理论基础上采用模糊综合评判方法,可以将用户的主观感知客观地评测出来,为运营商,内容提供商解决用户主观体验客观化提供了思路。     

基于内容感知的视频流媒体渐进式流传输方式分析模型

本文通过基于内容感知的视频流媒体渐进式流传输方式从而有效处理传输网络通道利用率以及平衡流媒体视频质量还有用户等待时间之间的冲突。在进行对视频内容分析还有理解的前提下,结合编码重要参数进行对视频内的每一帧的重要性的确定,借助MGS编码,进行在MobileIP测试平台予以实验。根据实验结果从而了解到该类型的传播方式具有一定的有效性,可以有助于网络通道高可用还有质量可伸缩的视频流媒体的高质量运行。     

基于LTE流媒体业务的用户体验质量评估方法研究

针对LTE流媒体业务用户体验质量的评估,本文基于层次分析法的区间估计理论,筛选出了影响用户感知相对重要的关键性能指标KPI,然后从技术因素和非技术因素两个角度分析,建立改进的用户体验质量评估模型。最后采用模糊层次分析法对关键质量指标权重KQI的计算方法进行改进,从而建立了一套完善的评估方法。仿真结果表明,该评估方法兼顾网络客观因素与用户主观感知,能够更好地反映实际的网络质量。     

基于媒体用户访问行为偏好模型的代理缓存算法

目前,代理缓存技术广泛应用于改善流媒体传输的服务质量.文章从实际用户日志文件的分析出发,利用发现的用户浏览流媒体对象时的行为分布模型,提出了一种新的视频流媒体缓存算法.仿真结果证明,该算法可以通过记录很少的用户访问信息获取较高的性能表现.     

面向移动内容分发网络环境的移动视频预取技术研究进展

随着移动互联网的发展和视频应用的普及,移动内容分发网络(MCDN)应运而生。预取技术是内容分发的关键技术之一,它通过预测用户未来的请求,提前将预测的内容预取至网络边缘,从而减少数据的获取时延、提升用户体验质量(QoE)。为深入阐述移动视频预取技术的发展现状,从用户移动行为感知、内容属性感知、网络资源感知等角度分析梳理了移动视频预取技术的最新研究成果,同时分析了各类预取方案的性能及其主要优缺点,最后指出未来可以进一步研究的方向。     

2011年十大预测

1、视频应用浪潮席卷互联网优酷上市成功及其在广告方面的优良表现印证了网络视频作为媒体及娱乐属性的发展可能,易观国际（Analysys International）预测2011年后,应用属性将逐步替代媒体属性加速网络视频发展。网络视频的本质是以流媒体方式展现丰富的内容,在广泛的吸引用户注意力后贩卖媒体价值是网络视频的一种属性,同时视频本身作为与文字、图片并行的表达形式,其应用属性也随着整个网络环境的提升以及用户需求的涌现而被逐步普及和重视。     

基于视觉显著计算的视频流媒体渐进式表达方法

为了有效解决视频流媒体传输网络带宽、播出视频质量和用户实时性访问之间的矛盾,本文提出了一种基于视觉显著计算的视频流媒体渐进式表达方法.在视频内容分析和理解的基础上,首先进行场景分类和视觉敏感区域提取;然后根据编码信息确定视频序列中各帧的重要性,估计帧内片层数据重要性;最后基于视觉显著计算的结果提出一种适应网络带宽和质量可伸缩的视频流媒体渐进式表达方法.采用中粒度质量可伸缩（MGS）编码,在模拟网络测试平台上分别针对集中式和分散式视觉敏感区域视频序列进行实验研究,实验结果验证了本文提出的基于视觉显著计算的视频流媒体渐进式表达方法的正确性和有效性.     

电信运营商IPTV用户感知提升方案的设计与应用

在IPTV视频业务的实际运营中,如何准确监测到用户在观看IPTV视频业务时的真实感知,及时发现视频质量问题、定位问题、解决问题,一直是IPTV业务运营关注的主要问题。本文介绍了一套IPTV用户感知提升解决方案,包括IPTV用户感知评估模型,基于该评估模型的IT系统建设,全自动IPTV用户感知优化流程再造,实施后达到IPTV用户感知显著提升的最终目标。     

影响流媒体系统视频质量的关键参数仿真测试研究

为了分析网络质量和视频编码参数对流媒体系统视频质量的影响,搭建了Windows Media Server和Flash Media Server两套流媒体仿真测试系统,通过网络带宽控制器仿真不同的视频传输网络环境,研究wmv和flv视频编码格式、码率、关键帧编码参数以及网络带宽、丢包率等网络传输参数对流媒体系统视频质量的影响,为流媒体系统提供参数优化依据.     

三网融合背景下4K超高清智能用户QoE感知系统应用研究

本文通过基于智能电视终端视频质量监测系统描述了4K超高清智能用户视频QoE感知的技术原理、技术实现及详细架构流程设计。针对4K电视业务的网络环境,给出了4K超高清智能用户QoE感知系统的改进方案,使4K新型视频业务能够实现高效精准的用户质量保障。     

A brief survey on adaptive video streaming quality assessment

Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.     

QoE in video streaming over wireless networks: perspectives and research challenges

The deployment of 3G/LTE networks and advancements in smart mobile devices had led to high demand for multimedia streaming over wireless network. The rapid increasing demand for multimedia content poses challenges for all parties in a multimedia streaming system, namely, content providers, wireless network service providers, and smart device makers. Content providers and mobile network service providers are both striving to improve their streaming services while utilizing advancing technologies. Smart device makers endeavor to improve processing power and displays for better viewing experience. Ultimately, the common goal shared by content providers, network service providers, and smart device manufactures is to improve the QoE for users. QoE is both an objective and a subjective metric measuring the streaming quality experience by end users. It may be measured by streaming bitrate, playback smoothness, video quality metrics like Peak to Signal Noise Ratio, and other user satisfaction factors. There have been efforts made to improve the streaming experiences in all these aspects. In this paper, we conducted a survey on existing literatures on QoE of video streaming to gain a deeper and more complete understanding of QoE quality metrics. The goal is to inspire new research directions in defining better QoE and improving QoE in existing and new streaming services such as adaptive streaming and 3D video streaming.     

移动网络中HTTP视频流终端数据分析及应用

伴随移动通信技术的发展,越来越多的用户选择通过移动网络随时随地观看网络视频,移动视频业务迅速增长.在现有的无线视频传输方式中,由于TCP的可靠性以及易穿透防火墙和网络地址转化等优点,基于TCP的HTTP流逐渐成为网络视频的主流传榆方式.另一方面,用户对业务体验需求的上升,也给移动数据的传输带来巨大挑战.该文通过采集和分析HTTP流的终端数据,获到了HTTP流在HSPA+中传输特性及关键参数的特征,分析结果表明其视频块传输速率变化与自回归模型近似,并在此基础上,提出一种视频等级自适应控制算法,能够有效地改善用户体验.     

HTTP自适应流媒体直播系统中的用户体验质量优化

分析HTTP自适应流媒体直播系统中对终端用户体验质量(QoE)产生影响的各类因素及其相互之间的作用关系,对基于服务器端、网络传输以及客户端的QoE优化策略进行总结。认为HTTP自适应流媒体直播系统的QoE优化重点在于降低延时,提出结合网络层和应用层影响因素来降低时延并提升用户QoE的建议。     

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.     

Research on Q-learning based rate control approach for HTTP adaptive streaming

HTTP adaptive streaming (HAS) has become the standard for adaptive video streaming service.In changing network environments,current hardcoded-based rate adaptation algorithm was less flexible,and it is insufficient to consider the quality of experience (QoE).To optimize the QoE of users,a rate control approach based on Q-learning strategy was proposed.the client environments of HTTP adaptive video streaming was modeled and the state transition rule was defined.Three parameters related to QoE were quantified and a novel reward function was constructed.The experiments were employed by the Q-learning rate control approach in two typical HAS algorithms.The experiments show the rate control approach can enhance the stability of rate switching in HAS clients.     

An objective and subjective quality assessment study of passive gaming video streaming

Passive gaming video‐streaming applications have recently gained much attention as evident with the rising popularity of many Over The Top (OTT) providers such as Twitch.tv and YouTube Gaming. For the continued success of such services, it is imperative that the user Quality of Experience (QoE) remains high, which is usually assessed using subjective and objective video quality assessment methods. Recent years have seen tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. A study on the performance of objective VQA on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos, is still missing. Towards this end, we present in this paper an objective and subjective quality assessment study on gaming videos considering passive streaming applications. Subjective ratings are obtained for 90 stimuli generated by encoding six different video games in multiple resolution‐bitrate pairs. Objective quality performance evaluation considering eight widely used VQA metrics is performed using the subjective test results and on a data set of 24 reference videos and 576 compressed sequences obtained by encoding them in 24 resolution‐bitrate pairs. Our results indicate that Video Multimethod Assessment Fusion (VMAF) predicts subjective video quality ratings the best, while Naturalness Image Quality Evaluator (NIQE) turns out to be a promising alternative as a no‐reference metric in some scenarios.     

A QoE centric distributed caching approach for vehicular video streaming in cellular networks

Distributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.     

一种基于罚因子的DASH调度算法

随着移动互联网的普及,基于DASH的流媒体传输协议的应用越来越广泛。如何在带宽波动较大的移动互联网环境中保证用户实现流媒体的流畅点播,提高用户的体验度是DASH调度算法最主要研究的问题。以提高用户体验度为出发点,结合带宽和缓存深度两方面因素,对带宽预测模型的置信度进行评价。在高置信度情况下,大胆地对网络带宽估计模型的模型参量进行调整;在低置信度情况下,以保护缓冲区深度为目的,谨慎地对模型参量进行调整。这种调整势必会对QoE造成相应的影响,该影响作为"罚因子"反馈回模型置信度的评价,以获得模型参数的动态最优解,得到一种较好的DASH调度算法。     

Slicing Wi-Fi links based on QoE video streaming fairness

Dynamic Adaptive Streaming over HyperText Transfer Protocol (DASH) video streaming is one of the dominant sources of traffic on the Internet, and this traffic is often delivered to users via Wi-Fi access points. This makes the quality of experience (QoE) of the service susceptible to degradation when the Wi-Fi link is underperforming, and it can also inflict QoE unfairness, creating situations where users connected to the same Wi-Fi network feel different levels of QoE. This article presents a system that improves the QoE fairness of DASH video transmission network slicing in the context of Wi-Fi networks. The proposed system slices the wireless resources unevenly among flows, taking into account the characteristics of the video and the QoE demands for each user. Experimental results show that fairness improves by 56% when compared to an unmanaged network, while the mean QoE is reduced by only 5%.