NEWTEC 卫星IP加速器PEP-Box 3000介绍

卫星IP加速器、TCP/IP加速、数据压缩/加密、多种业务不同优先及控制、可加速的VPN通过卫星和移动网络来访问公共IP网络已经日益被私人和企业用户所接受,并日益成为运营商的主要业务之一。但是,TCP和HTTP这两个在Internet上应用最广泛的协议,在像卫星网络这类高延时、高丢包率的系统中却只能提供非常低的传输效率,用户只能得到非常低     

一种基于评估反馈的视频流量分发策略

高品质的视频业务用户体验同时要求网络的低延迟和高速率,并且需要网络在较长一段时间内保持稳定的性能.通过对某省电信网络样本容量为45000的视频连接分析,当前的传输网络不能完全满足高质量视频传输的要求.针对上述问题,本文设计了一个基于从全局角度获取的客户端和网络状态进行用户体验评估的机制,用以确定视频流量的分发策略(传输路径、数据提供服务器和启动传输速率),以优化在不可靠的传输网上进行的视频传输.分析结果显示,该分发策略可有效降低视频业务的缓冲播放比和启动延迟,在高负载和负载波动剧烈的情况下,仍能有效发挥作用.     

基于FlashMediaServer的HTTP动态视频流技术

HTTP动态流技术是Adobe公司伴随FlashMediaServer4．5推出的一项最新的HTTP渐进下载技术,FlashMediaServer4．5能够将实时或预先录制的内容打包至可高速缓存的碎片中,这样可以支持完全的流媒体功能,结合HTTP动态流技术,充分利用现有的HTTP基础设施的优势,通过对视频源文件的特殊处理,可以将多比特率Flash流媒体内容更方便地呈现给流媒体用户,改善并提升用户的视频体验质量。     

基于机器学习的HTTP视频业务分类

随着多媒体技术的发展，优酷等HTTP视频业务占据越来越大的网络流量。对HTTP视频业务进行有效分类以保证其QoS已成为必要。然而大多数HTFP视频业务以浏览器为客户端，通过端口号难以将HTTP视频业务和其他Web业务区分开来。本文采用机器学习方法对HTTP视频业务进行区分，通过采用流组特征使得识别准确率提高了12％，并对三种机器学习     

移动网络中的流量加速技术

文章提出了采用多种流量加速技术,比如流量压缩和缓存技术、应用协议优化技术、区分业务和用户的带宽控制技术等,对移动数据流量进行管控、优化和疏导。多种流量加速技术的结合使用,可以减少流量增加对网络容量的冲击,提高用户体验。其中,流量压缩技术可以使得文本和图片的流量大大减少,并同时适配终端,满足移动用户的体验;流量缓存技术可以在本网内缓存热点内容,减少网间拥塞、用户访问时延及晚间的结算费用;超文本传输协议(HTTP)优化技术可以改善HTTP协议的性能,在不扩容的情况下提高移动网络的吞吐量。实验室测试和现网试点证明,上述技术和方法的应用可以取得比较好的移动数据流量管控、优化效果。     

自适应流媒体技术在CDN中的应用研究

内容分发网络(CDN)面临多终端、多业务融合所带来的各种技术挑战,自适应流媒体技术是一种结合了流控和HTTP渐进式下载的分发技术,它能够自动适应网络带宽,为用户提供良好的视频播放体验。论述CDN平台承载自适应流媒体业务所面临的技术挑战,并提出相应的应用解决方案,提高用户的视频服务体验,满足不断发展的视频服务需求。     

保障视频体验,家宽智能管道提速探讨

本文通过分析影响视频业务体验的关键因素,提出了基于TCP协议优化及DAA提速的一体化解决方案,详细描述了TCP协议优化及DAA提速的技术原理,以及在现网的实践结果,对运营商视频业务运营及用户体验提升有重要的借鉴意义.     

VoLTE业务的性能分析及优化研究

VoLTE主要是指需要将全面的业务承载在4G网络上使用的一种IP数据传输技术,通过在统一网络下,能够促进数据与语音业务的有机统一.通过使用VoLTE,为用户带来更低的接入时延,有助于提升用户的语音视频通话质量.因此,需要推动VoLTE发展,不断的强化无线频谱的利用效率,降低网络成本,提升用户体验效果,促进移动宽带语音业务的快速发展.本文针对VoLTE业务,对其自身的业务性能进行分析,并且提出了VoLTE业务的优化方法.     

基于视频ES流私有空间传输业务数据的方法

分析了视频ES流私有空间的组成方式,提出了一种基于视频ES流私有空间传输业务数据的方法。单一私有空间所能容纳的数据量较小,业务数据需划分成若干较小数据块后再传输,介绍了此数据块的数据结构。最后通过实验模拟从视频播出稳定性和业务数据传输可靠性两方面验证了此方法的可行性,该方法可以作为当前业务数据传输方法的一种补充。     

以降低HTTP页面时延为目标的参数优化

移动网络结构日趋复杂,网络场景多样化,影响用户体验的因素也越来越难以判断。结合SEQ平台系统,通过调整上行调度算法参数中"上行HARQ最大传输次数"参数,增大UE配置的最大传输次数,提高数据包传输成功率,降低信道编码率,提高解码效果,以降低HTTP页面打开时延。通过试验区优化验证,该措施能有效降低HTTP页面的各项时延。     

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.     

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.     

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.     

考虑时间-空间特性的流媒体视频QoE评价模型

流媒体视频服务作为目前网络服务最广泛的应用之一,其用户感知质量已经引起了服务提供商的广泛关注。文章提出了一种预测流媒体视频用户体验质量（QoE）的无参考评价模型。该模型基于人类视觉系统（HVS）,考虑视频时间-空间特性,讨论了编码压缩、网络丢包因素对视频质量的影响情况,与用户感知评分（MOS）相关度达到0.965,且二者均方根误差小于0.298,能够较为准确地预测流媒体视频用户感知质量。     

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.     

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。     

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.     

Improved video streaming using MSVC and nonoverlapping zone routing multipath propagation over MANETs

Providing real‐time video streaming in mobile ad hoc networks is difficult because of the time‐dependent channel status and stringent service requirements. The currently existing route request‐reply–based multihop overlay networks cause considerable control overheads in video transmission resulting in loss of data and communication breakdown. Such networks are more suitable to nonstreaming video applications rather than to time‐sensitive video streaming applications. Therefore, a powerful mechanism needs to be adopted to handle the channel failures amicably and reduce latency effectively in time critical video streaming applications over mobile ad hoc networks. In order to be resilient to the channel failures and reduce latency in such applications, 2 strategies, namely, multistate video coding and 2‐tier–based nonoverlapping zone routing multipath propagation through directional antennas have respectively been incorporated. The performance of the proposed nonoverlapping zone routing multipath propagation system is compared with those of the existing multicast zone routing and zone‐based hierarchical link state routing protocols with parameters average end‐to‐end delay, routing overhead and packet delivery ratio using NS 2.34. The simulation results show that latency and resilience get considerably improved. Finally, the video quality of the proposed work has been verified by subjective and objective video testing methods.     

视频流关键技术的研究进展

视频流是在因特网上进行视频信息传送的主流方式.为了在因特网上传输高质量的视频流,需要采取相应的传输机制.本文从视频流传输框架出发,系统讨论了当前视频流关键技术的研究进展,分析了各种技术的特点,并指出进一步发展的前景.     

Design and Implementation of a Network‐Adaptive Mechanism for HTTP Video Streaming

This paper proposes a network‐adaptive mechanism for HTTP‐based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time‐variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.