Internet上实时视频流的拥塞控制策略

分析了Intemet上实时视频传输的特点．提出了基于Intemet的实时视频流的拥塞控制策略,主要包括速率控制和速率整形,速率控制主要是根据网络运行状态预测当前可用的带宽,并根据预测值调整视频速率．以达到与可用带宽匹配：速率整形则是迫使发送端以码率控制算法规定的码率发送视频流。拥塞控制技术应用于终端系统并不需要路由器和网络的Qos支持．可以最大限度地提高视频质量。     

流媒体文件多码率自适应处理系统研究

为了应付流媒体系统应用网络带宽不足问题,设计了基于多码率文件传输技术和可变编解码参数Filters播放链路动态重连机制的自适应流媒体传输系统。该系统在网络带宽发生波动的条件下,能够自适应地选择与可用带宽相匹配码率的视频数据进行动态传输,进而减少视频数据缓冲等待次数和等待时间,有效提高网络带宽利用率和媒体内容服务质量,同时采用"最短"的链路进行媒体内容的动态重连,避免Filters动态重连中出现"卡塞感",有效提高流媒体系统的服务质量。     

基于丢包率统计的无线流媒体码率动态适配技术的研究

随着移动通信技术的不断发展,流媒体视频在无线环境下的应用得到普及。然而,移动网络环境所存在的不稳定性可能导致误码率高以及阻塞丢失严重的情况,从而严重影响用户的观看体验。因此,如何在现有移动网络环境发生频繁切换或网络较不稳定的情况下,仍使用户获得较好的流媒体视频播放体验,成了迫切需要解决的课题。本文研究提出了一种基于丢包率统计的无线环境下流媒体码率动态适配的方法,能够根据网络环境条件为用户重新选择适合用户的播放体验的视频码率,从而保证视频的传输质量和流畅度,优化用户的视频体验。     

基于TCP协议的音频流媒体QoE的定量研究

流媒体接收端音频的初始延时或中断,以及采用自适应播放产生的播放速率变化是基于TCP协议的音频流媒体的用户体验质量下降的主要原因。介绍了一种中断强度的概念,并通过模拟实际网络环境的主观音质测试方法来分析中断强度对QoE的影响。结果表明,在可用带宽发生变化时,中断强度和主观音质测试得分有很好的相关性,平均相关系数为-0.9。从而表明中断强度可以作为评价音频流媒体QoE的客观评价参数。此外,还对播放速率变化对QoE的影响进行了主观音质测试,变速算法为波形相似叠加算法。测试结果显示,当变速因子超过105%时,音质下降严重,测试结果可以作为自适应播放中参数设置的参考。     

面向ICN的动态自适应流媒体算法

信息中心网络(information-centric networking,ICN)与传统IP网络的重要区别在于,前者的路由器节点具备缓存能力,能将热门内容推送到更靠近客户的网络边缘,从而降低客户获取内容的时延。然而ICN中的节点缓存特性会导致传统的自适应流媒体协议出现视频抖动甚至播放中断,降低客户的观看体验。提出了ICN中的动态自适应流媒体算法,在保证客户端播放流畅的同时,尽可能地提升视频片段的码率,提升客户的观看体验。通过在现有的ICN平台中进行仿真实验,证明了该自适应算法在性能上优于传统的算法。     

基于P2P流媒体直播系统的数据传输策略

针对P2P模式下视频流媒体直播系统的数据传输策略用改进的模拟退火算法进行了优化。以全局规划的思想建立了P2P视频流媒体的数据传输策略数学模型VMDTSA-P2P,模型充分考虑了可用出口带宽和可用性时间对播放连续性的影响,把它们作为获取最优解的目标函数因子,并用改进后的模拟退火算法进行解的寻优,相比于传统的数据传输策略,在具有大量用户的情况下加快了新节点从伙伴节点中选择数据块提供节点的速度,并且保证得到近似最优的数据块提供节点组合,减少了视频流媒体直播系统播放的延迟,从而提高了播放的连续性、流畅性,保证了视频播放的质量。通过该算法在模拟P2P系统中的实现,验证了该算法在P2P系统中对数据传输的准确性和高效性。     

无线视频通信中基于队列预测的自适应缓存管理算法

无线网络中实时视频业务对网络环境的变化非常敏感,因此通常需要在接收端采用合理的缓存管理策略来缓解网络波动对用户观看视频造成的影响。提出一种基于队列预测的自适应缓存播放管理机制,该方法通过判断当前缓存队列的状态,并根据实时视频到达率和端到端时延对缓存长度以及播放速率进行综合调整。实验结果表明,所提算法可以随着网络环境的波动自适应地调整缓存大小和播放速率,有效降低视频业务的中断频率和丢帧率。     

基于Internet的实时视频流的应用层QoS控制策略

分析了Internet上实时视频传输的特点，提出了基于Internet的实时视频流的应用层QoS控制策略，主要包括拥塞控制策略和差错控制策略以及相应的控制技术。在拥塞控制中，讨论速率控制和速率整形，速率控制主要是根据网络运行状态预测当前可用的带宽，并根据预测值调整视频速率，达到与可用带宽匹配；速率整形则是迫使发送端以码率控制算法规定的码率发送视频流。在差错控制中，则讨论了编码器差错复原、解码器错误隐藏和编码器／解码器交互的差错控制等控制策略。这些控制技术应用于终端系统并不需要路由器和网络的QoS支持，可以最大限度地提高视频质量。     

因特网环境下MPEG-4视频自适应码率控制方案

介绍了因特网上一种端到端的MPEG-4视频网络传输结构的自适应码率控制方案.结合RTP/RTCP协议,通过终端系统的反馈控制算法,在视频发送端的MPEG-4编码器中由合适的码率调整算法自适应地调整输出码率,从而最大限度地利用网络带宽资源,并且保证视频传输的视觉感知质量.     

对主流自适应码率流媒体技术的分析

伴随着互联网多媒体应用的快速增长,尤其是在线视频业务的不断发展,传统的流媒体技术由于其单一的编码方式难以适应差异化的用户网络变化,极大地影响了用户体验。分析了当前在线视频业务面临的主要问题,同时介绍了新兴的自适应码率流媒体技术的特点和优势。通过对主流自适应码率流媒体和传统流媒体技术的分析和比较,表明对于OTT业务而言,标准化的自适应码率流媒体技术比传统的流媒体技术更加有优势。     

Quality-driven cross-layer optimized video delivery over LTE

3GPP Long Term Evolution is one of the major steps in mobile communication to enhance the user experience for next-generation mobile broadband networks. In LTE, orthogonal frequency- division multiple access is adopted in the downlink of its E-UTRA air interface. Although cross-layer techniques have been widely adopted in literature for dynamic resource allocation to maximize data rate in OFDMA wireless networks, application-oriented quality of service for video delivery, such as delay constraint and video distortion, have been largely ignored. However, for wireless video delivery in LTE, especially delay-bounded real-time video streaming, higher data rate could lead to higher packet loss rate, thus degrading the user-perceived video quality. In this article we present a new QoS-aware LTE OFDMA scheduling algorithm for wireless real-time video delivery over the downlink of LTE cellular networks to achieve the best user-perceived video quality under the given application delay constraint. In the proposed approach, system throughput, application QoS constraints, and scheduling fairness are jointly integrated into a cross-layer design framework to dynamically perform radio resource allocation for multiple users, and to effectively choose the optimal system parameters such as modulation and coding scheme and video encoding parameters to adapt to the varying channel quality of each resource block. Experimental results have shown significant performance enhancement of the proposed system.     

一个基于速率控制的Internet视频流服务方案

由于视频流服务对于网络服务质量有着较高的要求,而现有的Internet所提供的是尽力而为的服务,无法保证数据的实时传输。该文设计了一个用于Internet上视频流的端到端传输方案.整个方案设计的目的是在网络本身缺乏服务质量保证的条件下尽可能达到最好的视频传输质量。根据可用带宽估计和网络信息反馈,系统对发送速率进行调整,并提供两种视频流服务:存储视频和实时视频。仿真结果表明方案的性能良好,能满足Internet视频流的需求。     

Segment‐aware dynamic routing for DASH flows over software‐defined networks

Most of the video streaming applications running over the Internet send video data over HTTP and provide an architecture for video clients to adapt video quality during streaming. In HTTP adaptive streaming, a raw video is encoded at various qualities, each encoded video file is divided into small segments, and the clients may change the segment quality by sending requests for segments having different qualities over time. MPEG has standardized dynamic adaptive streaming over HTTP (MPEG‐DASH) due to this tendency. In this work, we focus on DASH over software‐defined networks (SDN), and we dynamically reroute DASH flows by considering the current network capacity, available bandwidth of the paths, and bitrate of the segments in order to provide high quality of experience (QoE) and fairness among DASH clients. Simulations performed under various network conditions show that the proposed study provides higher QoE and fairness compared with the max‐flow routing approach.     

An experimental evaluation of rate-adaptive video players over HTTP

Adaptive (video) streaming over HTTP is gradually being adopted by content and network service providers, as it offers significant advantages in terms of both user-perceived quality and resource utilization. In this paper, we first focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming and Netflix) and one open-source player (Adobe's OSMF). We first examine how the previous three players react to persistent and short-term changes in the underlying network available bandwidth. Do they quickly converge to the maximum sustainable bitrate? We identify major differences between the three players and significant inefficiencies in each of them. We then propose a new adaptation algorithm, referred to as AdapTech Streaming, which aims to address the problems with the previous three players. In the second part of the paper, we consider the following two questions. First, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share that resource in a stable and fair manner? And second, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay, keeping the viewing experience “live”?     

Adaptive available bandwidth estimation for internet video streaming

In this study, an adaptive available bandwidth estimation approach that is suitable for Internet video streaming is developed. The algorithm exploits repetitive measurements and uses this redundancy to improve its video adaptation decision. The importance of available bandwidth estimation in Internet applications has recently increased particularly because of the heterogeneity of the network links. Many of the Internet paths may contain wired and wireless links in which loss may happen due to congestion as well as link errors. Hence, loss rate by itself is not a sufficient statistics for monitoring purposes. If the loss is due to congestion, video quality can then be decreased whereas if the loss is due to link error, no such action is necessary. Moreover, in video streaming, such an estimate can be used to determine the new video rate if the quality is to be increased. In our approach, active probing packets are used to estimate bandwidth in very short time duration. The novelty of our estimator is its adaptivity in the sense that the overhead caused by the estimator is automatically reduced when congestion builds up. The trade off is reduced accuracy. Such accuracy is not needed under congestion anyway and when things get back to normal, our estimator turns back to normal operation mode. We have integrated our algorithm into our video streamer and carried out experiments on both simulated and actual streaming applications on the Internet. The results indicate that our estimator algorithm increases streaming performance substantially.     

Resource allocation algorithm for LTE networks using fuzzy based adaptive priority and effective bandwidth estimation

In this paper, we propose a scheme to allocate resource blocks for the Long Term Evolution (LTE) downlink based on the estimation of the effective bandwidths of traffic flows, where users’ priorities are adaptively computed using fuzzy logic. The effective bandwidth of each user traffic flow that is estimated through the parameters of the adaptive β-Multifractal Wavelet Mode modeling, is used to attain their quality of service (QoS) parameters. The proposed allocation scheme aims to guarantee the QoS parameters of users respecting the constraints of modulation and code schemes (modulation and coding scheme) of the LTE downlink transmission. The proposed algorithm considers the average channel quality and the adaptive estimation of effective bandwidth to decide about the scheduling of available radio resources. The efficiency of the proposed scheme is verified through simulations and compared to other algorithms in the literature in terms of parameters such as: system throughput, required data rate not provided, fairness index, data loss rate and network delay.     

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.     

Long-term rate control for concurrent multipath real-time video transmission in heterogeneous wireless networks

Concurrent multipath transmission provides an effective solution for streaming high-quality mobile videos in heterogeneous wireless networks. Rate control is commonly adopted in multimedia communication systems to fully utilize the available network bandwidth. This paper proposes a novel rate control for concurrent multipath video transmission. The existing rate control algorithms mainly adapt bit rate in the short-term pattern, i.e., without considering the long-term video transmission quality. We propose a long-term rate control scheme that takes into account the status of both the transmission buffer and video frames. First, a mathematical model is developed to formulate the non-convex problem of long-term quality maximization. Second, we develop a dynamic programming solution for online encoding bit rate control based on buffer status. The performance evaluation is conducted in a real test bed over LTE and Wi-Fi networks. Experimental results demonstrate that the proposed long-term rate control scheme achieves appreciable improvements over the short-term rate control schemes in terms of video quality and delay performance.     

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.