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.     

Performance of MIMO-UWB for LOS and N-LOS Propagation

Mobile cloud computing (MCC) enables ubiquitous access to a diverse range of Internet multimedia services in a pay-as-you-go economic model. In an MCC environment with highly mobile users, the migration of service requests from one cloud server to another due to user movement may frequently occur. We note that when the load offered to the cloud server is increased beyond the capacity limit, particularly when migrated traffic due to user movement suddenly appears, the probability to disrupt existing services gets higher, consequently resulting in the degradation of user quality of experience (QoE). To keep the service disruption probability at an acceptable level so as to maintain a high user-perceived QoE for different classes of multimedia services, this paper proposes a QoE-aware service continuity strategy for the cloud server in an MCC environment. The strategy is based on the buffer-occupancy threshold policy that differentiates newly arriving service requests coming from the mobile users and offers effective protection for migrated service requests against traffic fluctuation in newly arriving service requests. With the proposed strategy, the cloud server can dynamically change the buffer thresholds for different classes of service requests based on the offered traffic load and the user mobility to improve resource utilization, and, most importantly, to keep the service disruption probability at an acceptable level. Besides, by taking the effect of migrated traffic into account, we develop an analytical model to study the performance of the cloud server using the proposed strategy. With the analytical model, we propose an iterative method to determine the optimal buffer thresholds that maximize resource utilization while keeping an acceptable user QoE for different classes of services.

     

QoE-aware video cooperative caching and transmission mechanism based on TWDM-PON and C-RAN

The quality of user experience suffers from performance deterioration dramatically due to the explosively growing data traffic.To improve the poor performance of cell-edge users and heavy-load cell users,which caused by dense network and load imbalance respectively,an QoE-aware video cooperative caching and transmission mechanism in cloud radio access network was proposed.Cooperative gain-aware virtual passive optical network was established to provide cooperative caching and transmission for video streaming by adopting collaborative approach in optical domain and wireless domain.Furthermore,user experience for video streaming,bandwidth provisioning and caching strategy were jointly optimized to improve QoE,which utilized the methods of dynamic caching in optical domain and buffer level-aware bandwidth configuration in wireless domain.The results show that the proposed mechanism enhances the quality of user experience and effectively improves the cache hit rate.     

IPTV Systems, Standards and Architectures: Part II - Heterogeneous Networks and Terminal-Aware QoS/QoE-Guaranteed Mobile IPTV Service

We describe two main technical challenges in terms of the network and application levels for mobile IPTV service. To support a multimedia- streaming application service over a mobile network environment, we present the IP/MPLS-based NGN architecture with fixed and mobile convergence and its challenge, which is basically to support QoS for mobile IPTV ser vice. To meet the challenges for application source levels, we focus on the relationship of user terminals and video streaming sources to support user-perceived QoE, based on high- quality service between heterogeneous terminals. The techniques to meet these challenges depend on the characteristics of media processing and terminal capabilities, such as LCD panel size, resolution, video coding, and so on, in the heterogeneous network environment. To have a relationship between the two different levels, the significant factors mentioned previously are required to deliver the bandwidth coupled with a user profile to provide QoS/QoE in assured seamless mobile IPTV service on client devices.     

Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

HTTP adaptive streaming (HAS) is becoming the de facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. It has been shown that state‐of‐the‐art heuristics perform suboptimal when sudden bandwidth drops occur, therefore leading to freezes in the video playout, the main factor influencing users' quality of experience (QoE). This issue is aggravated in case of live events, where the client‐side buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In this article, we propose a framework capable of increasing the QoE of HAS clients by reducing video freezes. The framework is based on OpenFlow, a widely adopted protocol to implement the software‐defined networking principle. An OpenFlow controller is in charge of introducing prioritized delivery of HAS segments, based on the network conditions and the HAS clients' status. Particularly, the HAS clients' status is obtained without any explicit clients‐to‐controller communication, and thus, no extra signaling is introduced into the network. Moreover, this OpenFlow controller is transparent to the quality decision process of the clients, as it assists the delivery of the segments, but it does not determine the actual quality to be requested. In order to provide a comprehensive analysis of the proposed approach, we investigate the performance of the proposed OpenFlow‐based framework in the presence of realistic Internet cross‐traffic. Particularly, we model two types of applications, namely, HTTP web browsing and progressive download video streaming, which currently represent the majority of Internet traffic together with HAS. By evaluating this novel approach through emulation in several multi‐client scenarios, we show how the proposed approach can reduce freeze time for the HAS clients due to network congestion up to 10 times compared with state‐of‐the‐art heuristics, without impacting the performance of the cross‐traffic applications. Copyright © 2016 John Wiley & Sons, Ltd.     

无线网络中的QoS和策略管理测试(续一)

上一期中,我们论述了策略管理和QoS在无线网络中的作用,并介绍了3GPP所定义的实现方式。在本期中,我们将继续讨论QoS和策略管理的测试方法,并介绍IXIA在一个北美运营商实验室里实现VoLTE测试的成功案例。6无线网络的服务质量验证全球运营商正花费数十亿美元购买设备和频谱来升级他们的移动宽带网络。通过网络升级,运营商旨在     

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%.     

Fine-Grained Video Traffic Classification Based on QoE Values

The paper proposes a set of features suitable for fine-grained traffic classification of network video, with data collected from real network. These features are parameters related to quality of experience (QoE), which reflects the user’s perception. The QoE value is calculated based on the ITU-T P.1201/Amd2 standard. Under this standard, each video flow can calculate corresponding QoE value and its probability of distribution. One innovative aspect of the paper is that the characteristics of QoE value and its probability distribution are extracted as the discriminating features which are suitable for video traffic classification. The extracted features of QoE distribution are typically mean, variance, maximum and minimum statistical characteristics, and the probability distribution of features can be obtained. Different from previous work, in our method, we obtain for the first time the discrete distribution of probability with five values, and use them directly as independent features to participate in feature selection and classification. The experimental results demonstrate that the proposed new features can significantly improve classification accuracy compared with an existing method.

     

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.     

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.     

Quality of experience (QoE) driven adaptation scheme for?voice/video over IP

Network quality of service (NQoS) of IP networks is unpredictable and impacts the quality of networked multimedia services. Adaptive voice and video schemes are therefore vital for the provision of voice over IP (VoIP) services for optimised quality of experience (QoE). Traditional adaptation schemes based on NQoS do not take perceived quality into consideration even though the user is the best judge of quality. Additionally, uncertainties inherent in NQoS parameter measurements make the design of adaptation schemes difficult and their performance suboptimal. This paper presents a QoE-driven adaptation scheme for voice and video over IP to solve the optimisation problem to provide optimal QoE for networked voice and video applications. The adaptive VoIP architecture was implemented and tested both in NS2 and in an Open IMS Core network to allow extensive simulation and test-bed evaluation. Results show that the scheme was optimally responsive to available network bandwidth and congestion for both voice and video and optimised delivered QoE for different network conditions, and is friendly to TCP traffic.     

Dissecting the performance of YouTube video streaming in mobile networks

Video streaming applications constitute a significant portion of the Internet traffic today, with mobile accounting for more than half of the online video views. The high share of video in the current Internet traffic mix has prompted many studies that examine video streaming through measurements. However, streaming performance depends on many different factors at different layers of the TCP/IP stack. For example, browser selection at the application layer or the choice of protocol in transport layer can have significant impact on the video performance. Furthermore, video performance heavily depends on the underlying network conditions (eg, network and link layers). For mobile networks, the conditions vary significantly, since each operator has a different deployment strategy and configuration. In this paper, we focus on YouTube and carry out a comprehensive study investigating the influence of different factors on streaming performance. Leveraging the Measuring Mobile Broadband Networks in Europe (MONROE) test bed that enables experimentation with 13 different network configurations in four countries, we collect more than 1800 measurement samples in operational mobile networks. With this campaign, our goal is to quantify the impact of parameters from different layers on YouTube's streaming quality of experience (QoE). More specifically, we analyze the role of the browser (eg, Firefox and Chrome), the impact of transport protocol (eg, TCP or QUIC), the influence of network bandwidth, and signal coverage on streaming QoE. Our analysis reveals that all these parameters need to be taken into account jointly for network management practices, in order to ensure a high end‐user experience.     

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.     

QoE‐based pseudo heartbeat message compression mechanism for future wireless network

Wireless multimedia services are increasingly becoming popular, which boosts the need for better quality‐of‐experience (QoE). However, there are many aspects leading to the degradation of real‐time video QoE, especially, a large number of always‐on‐line (AOL) applications existing in future wireless networks transmit heartbeat message periodically to keep always‐on, and hence induce heavy signaling costs and overload wireless networks. In this paper, we propose QoE‐based pseudo heartbeat message compression mechanism to reduce the number of signaling loads in the radio access network by intercepting the heartbeat message at a certain frequency in the proxy client. To maintain the protocol feature of the AOL applications, the heartbeat messages are reconstructed by the proxy server and sent to the application server. Furthermore, to analyze the influence of this mechanism on the video user, a new QoE perception model is proposed. Finally, combined the QoE perception model for video services with AOL services, the utility function for joint optimization multi‐services is developed to determine the optimum compression frequency. The simulation results show that the proposed mechanism greatly alleviates the signaling load and leads to a significant performance improvement on the QoE of video users, while a slight decrease in the QoE of AOL users. Copyright © 2016 John Wiley & Sons, Ltd.     

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。     

魏芳,顾鹏南

随着移动互联网的发展和4G网络的成熟,移动视频业务已逐步成为主流业务,移动视频已成为互联网企业抢占的制高点,运营商在其中如何定位需要深入思考。本研究首先立足网络本身,在4G环境下研究视频感知监测、缓存、动态转码等一系列技术手段,建立视频质量优化与保障体系。在此基础上,探索构建3GPP标准的AAC(Application Access Control,应用接入控制)架构,结合PCC(Policy and Charging Control,策略及计费控制)功能,通过标准接口将网络QoS(Quality of Service,业务质量)能力向互联网公司开放,参与视频的业务调度过程,进而具备分享业务价值链的能力。     

Shielding video streaming against packet losses over VANETs

Vehicular ad-hoc networks (VANETs) are being widely adopted in the last few years. This type of network enables the utilization of a large diversity of distributed applications, such as road and traffic alerts, autonomous driving capabilities and video distribution. Video applications can be considered one of the most demanding services because it needs a steady and continuous flow of information. This presents a set of challenges to VANETs considering their scarce network resources due to the vehicle movement and time-varying wireless channels. Considering the above mentioned issues, an adaptive quality of experience (QoE)-driven mechanism is needed to provide live transmission capabilities to video-equipped vehicles. This mechanism has to overcome the challenges to grant a high-quality video transmission without adding any unnecessary network overhead. To this end, a forward error correction (FEC) technique can be adapted to enhance the video distribution, leading to higher QoE for end users. The proposed self-adaptive FEC-based mechanism (SHIELD) uses several video characteristics and specific VANETs details to safeguard real-time video streams against packet losses. One of the main contributions of this work is the combined used of network density, signal-to-noise ratio, packet loss rate, and the vehicle’s position. This allows SHIELD to better protect the video sequences and enhance the QoE. In doing that, we are able to improve the user experience, while saving network resources. The advantages and drawbacks of the proposed mechanism are demonstrated through extensive experiments and assessed with QoE metrics, proving that it outperforms both adaptive and non-adaptive mechanisms.     

流量业务用户感知的评估方法分析

本文探寻移动数据业务网络参数、性能指标与用户体验之间的关系,分析影响用户感知的因素,提出用现网数据量化用户体验的方法。对于移动数据业务,我们将从移动用户体验,即感知质量QoE的角度出发,分析现网数据业务特征,研究影响业务QoE关键因素,抽象数据业务类型,探索评估各类业务用户感知质量评估的理论依据。     

基于Jaccard的移动终端自动识别并行算法及其MapReduce实现

The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers’ behaviours and enhance their user experience. In this paper, we propose a novel method for mobile device model recognition by using statistical infor-mation derived from large amounts of mobile network traffic data. Specifically, we create a Jaccard-based coefficient measure method to identify a proper keyword representing each mobile device model from massive unstruc-tured textual HTTP access logs. To handle the large amount of traffic data generated from large mobile networks, this method is designed as a set of parallel algorithms, and is imple-mented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features. Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and pro-ducer-independency requirements of large mobile network operators. Results show that a 91.5% accuracy rate is achieved for rec-ognising mobile client models from 2 billion records, which is dramatically higher than existing solutions.     

Discrete quality assessment in IPTV content distribution networks

Maintaining the quality of videos in resource-intensive IPTV services is challenging due to the nature of packet-based content distribution networks (CDN). Network impairments are unpredictable and highly detrimental to the quality of video content. Quality of the end user experience (QoE) has become a critical service differentiator. An efficient real-time quality assessment service in distribution networks is the foundation of service quality monitoring and management. The perceptual impact of individual impairments varies significantly and is influenced by complex impact factors. Without differentiating the impact of quality violation events to the user experience, existing assessment methodologies based on network QoS such as packet loss rate cannot provide adequate supports for the IPTV service assessment. A discrete perceptual impact evaluation quality assessment (DEQA) framework is introduced in this paper. The proposed framework enables a real-time, non-intrusive assessment service by efficiently recognising and assessing individual quality violation events in the IPTV distribution network. The discrete perceptual impacts to a media session are aggregated for the overall user level quality evaluation. With its deployment scheme the DEQA framework also facilitates efficient network diagnosis and QoE management. To realise the key assessment function of the framework and investigate the proposed advanced packet inspection mechanism, we also introduce the dedicated evaluation testbed—the LA2 system. A subjective experiment with data analysis is also presented to demonstrate the development of perceptual impact assessment functions using analytical inference, the tools of the LA2 system, subjective user tests and statistical modelling.