IP城域网IPTV业务的技术保障探讨

本文阐述了IPTV网络的现状和压力,为保障IPTV业务做出的网络优化保障,以及保障实施后的现网效果测评,对下一代维护的建议等。     

新联通如何将比较优势转化为市场优势

文章指出新联通与其他两个运营商相比，在WCDMA建网和运营初期具有比较优势，但不会自动转化为市场竞争优势，由此从品牌塑造、建立用户体验体系（QoE）、向标准主导者转型三方面出发，详细阐述了如何培育和壮大新联通的核心竞争力。     

Research on improving the QoE of relay users in cooperative communication systems

Customers'satisfaction with services is reflected by quality of experience (QoE). Insofar, most studies on cooperative communication have been focused on improving the QoE of source users. However, the improvement of a source user's QoE is obtained at cost of degradation of the relay user's QoE. On the other hand, cooperative communications can achieve performance similar to that of a conventional multiple-input multiple-output (MIMO) system by forming virtual M1M0 arrays. Hence, to improve the QoE performance of relay users, this article proposes the concept of a belief threshold at the destination user and a new cooperative scheme based on the belief threshold destination (BTD) technique, while without decaying the BER performance of the communication system.     

Evolution of satellite communications: Integration of ETSI BSM and DVB‐RCS for future satellite terminals

The main added value of the European Telecommunications Standards Institute broadband satellite multimedia (ETSI BSM) architecture is the definition of the Satellite Independent‐Service Access Point (SI‐SAP) protocol interface, which formally separates Satellite Dependent (SD) from SI layers, thus enabling the implementation of powerful vertical QoS mapping strategies. On the other hand, DVB‐S2/RCS satellite standard is considered the driving technology to integrate satellite with terrestrial infrastructure and provide up‐to‐date services. This paper focuses on the integration of ETSI BSM architecture and DVB‐RCS technology, by analysing the adaptations needed on real DVB‐RCS terminals to be interoperable with the SI‐SAP interface. To this end, the detailed design of an underlying architecture taking into account required adaptations and new functionalities is proposed. The possible further evolutions of the BSM specification are also highlighted, showing the potential for the development of future devices integrating both DVB‐RCS and ETSI BSM architectures also in view of the recent upgrade to the DVB‐RCS2 standard. The paper also validates the SI‐SAP QoS functionalities and proves the performance benefits in terms of QoS and quality of experience of Web‐browsing by means of a satellite emulator developed fors this aim. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

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.     

关于人工智能对用户QoE提升的若干思考

认为网络视频供应商也开始更加关注用户体验质量(QoE),但数据量大、个性化提升用户QoE困难等问题仍存在。梳理了人工智能(AI)提升大数据时代下用户QoE的系统化解决方案。以视频业务中的流媒体码率自适应任务为例,将基于用户行为分析的个性化QoE模型及时"反馈"到深度学习神经网络中,通过模型自适应决策和码率调节,实现用户QoE最大化。最后指出基于人工智能的QoE提升仍然存在一些挑战。     

面向多业务用户体验的基站节能策略研究

随着ICT行业的快速发展,无线通信网络的密集部署和应用普及,随之带来的高能耗和碳排放问题给环境造成了巨大影响。用户关联的基站关闭策略能有效减少能耗,现有策略主要针对用户接收服务质量进行方法设计,并未充分考虑具体业务特征以及用户体验。引入了用户体验质量指标QoE,提出了一种面向多业务用户体验的基站节能策略ES-MSUE。采用非线性整数规划对问题进行建模,实现了用户体验和能耗之间的均衡。通过与其他相关基站关闭策略进行对比实验,结果表明提出的方法具有较好的节能效果和用户体验质量。     

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.     

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.