Enhancing aggregate QoS for video streaming

The IETF's Differentiated Services architecture is designed to provide different types or levels of service for Internet traffic. One of its key features is that traffic flows are aggregated so that routers in the core network only need to distinguish a relatively small number of aggregate flows, even if those flows consist of hundreds or thousands of individual flows. However, network-level QoS differentiation may not satisfy the requirements of many QoS-sensitive applications such as live video streaming, where end-to-end delay and reliability must be guaranteed.

This paper investigates application-level service differentiation for MPEG video streaming in a Diff-Serv-aware MPLS network infrastructure, along with routing support at the network layer. Based on the fact that MPEG video has become one of the most popular formats for Internet (wired and wireless) users, our approach enhances aggregate QoS for video streaming by employing existing application-level knowledge of the MPEG video structure; therefore it requires neither a new video compression algorithm nor additional bandwidth. Several MPEG video dispersion models are proposed and analyzed. Simulation results show that two such strategies are superior to the other tested. In addition, a new routing scheme is proposed to support searching the ‘best’ paths for efficient multi-path video streaming. Through extensive simulations, we demonstrate that our approaches improve the aggregate QoS of MPEG video streams, as well as overall network efficiency.     

基于MDP的移动流媒体能耗优化方法

针对移动流媒体应用的高能耗问题,面向异构平台环境,提出了基于马尔科夫决策过程的移动流媒体能耗优化方法。通过综合不同用户使用习惯、视频播放功耗、播放时长及当前网络状态,基于最大奖励原则对CPU资源进行合理分配,以达到降低功耗的目标。实验结果表明,相比于系统默认异核多处理调度策略,提出的能耗优化方法能在保持流媒体视频播放质量的同时,有效降低其在移动终端的能耗。     

Cross-layer optimization for many-to-one wireless video streaming systems

This paper develops a cross-layer optimization solution for video streaming from multiple sources to a central proxy station over a wireless network. The proposed solution manages the application rates and transmission opportunities of various video sources based on the dynamic network conditions in such a way that minimizes the overall video distortion. The solution includes a new online approach for estimating the effective network airtime and a new video bitrate-distortion model. We demonstrate the effectiveness of the proposed solution through extensive experiments. The results show that the proposed solution substantially enhances the perceptual video quality while reducing the power consumed by the video sources and that the solution is highly adaptable to the existence of interfering network traffic.     

Throughput optimization for video streaming proxy servers based on video staging

A video streaming proxy server needs to handle hundreds of simultaneous connections between media servers and clients. Inside, every video arrived at the server and delivered from it follows a specific arrival and delivery schedule. While arrival schedules compete for incoming network bandwidth, delivery schedules compete for outgoing network bandwidth. As a result, a proxy server has to provide sufficient buffer and disk cache for storage, together with memory space, disk space and disk bandwidth. In order to optimize the throughput, a proxy server has to govern the usage of these resources. In this paper, we first analyze the property of a traditional smoothing algorithm and a video staging algorithm. Then we develop, based on the smoothing algorithm, a video staging algorithm for video streaming proxy servers. This algorithm allows us to devise an arrival schedule based on the delivery schedule. Under this arrival and delivery schedule pair, we can achieve a better resource utilization rate gracefully between different parameter sets. It is also interesting to note that the usage of the resources such as network bandwidth, disk bandwidth and memory space becomes interchangeable. It provides the basis for inter-resource scheduling to further improve the throughput of a video streaming proxy server system.

一种移动网络视频流U-vMOS分类算法

针对传统分类算法无法有效解决高度不平衡移动网络视频流U-vMOS分类问题,提出基于代价敏感(cost-sensitive)思想改进AdaBoost算法构造U-vMOS分类器,在迭代过程中,于样本权重更新环节引人代价敏感因子,增加对少数类(mi-nority)关注,最终 U-vMOS 分类器获得优秀 的查准率和查全率.实验结果表明,在数据非均衡条件下,改进AdaBoost算法F-Measure值和稳定性明显优于其它算法,能够为运营商和视频产业链提升用户体验给出有用的指导建议.     

Joint optimization of continuity and quality for streaming video

This paper aims to reduce the amount of prebuffering required to ensure a maximum video continuity in streaming. Current approaches do this by slowing the playout frame rate of the decoder, this is known as adaptive media playout (AMP). However, doing this introduces playout distortion to the viewers as the video is played slower than its natural playout rate. We approach this by proposing a frame rate control scheme that jointly adjusts the encoder frame generation rate of the encoder and the playout frame rate of the decoder. In addition to using AMP to improve video continuity, we also allow the encoder to increase the encoder frame generation rate. This means the encoder will be sending more frames to the decoder to quickly increase the number of frames available at the playback buffer, thus lowering the chance of buffer underflow which causes discontinuity in video playback. At the same time, the increase in the number of frames at the playback buffer may mean that the decoder does not need to use AMP to delay the playback, thus lowering the playback distortion. However, the increase in encoder frame generation rate comes at a price because frame quality will need to decrease in order to meet the constraint on available network bandwidth. This implies that the scheme needs to find the optimal trade-off between frame quality, playout distortion and video continuity. To do that, we characterize the frame rate control problem using Lyapunov optimization. We then systematically derive the optimization policies. We also show that these policies can be decoupled into separate encoder and decoder optimization policies, thus allowing for a distributed implementation. Simulation results show significant reductions in the prebuffering requirements over a scheme that perform no frame rate control and lower playout distortions compared to the AMP schemes, while exhibiting a modest drop in frame quality.     

QoE prediction model for mobile video telephony

Interactive online video applications, such as video telephony, are known for their vulnerability to network condition. With the increasing usage of hand-held wireless mobile devices, which are capable of capturing and processing good quality videos, combined with the flexibility in an end-user movements have added new challenging factors for application providers and network operators. These factors affect the perceived video quality of mobile video telephony applications, unlike conventional video telephony over desktop computers. We investigate this impact on video quality of mobile video telephony in varying network conditions and end-users movement scenarios. Based on 312 live traces, we quantitatively derive the correlation between the perceived video quality and the network Quality of Service (QoS) and user mobility. With the results, we develop a Quality of Experience (QoE) prediction model for mobile video telephony using Support Vector Regression techniques. The prediction models display ≈ 0.8 pearson correlation with experimental data. Our methodology and findings can be used to guide the video telephony application providers and network operators to work towards satisfying end-user experience.     

L-Shape advertising for mobile video streaming services: Less intrusive while still effective

Advertising is significant in video streaming services on mobile devices, as well as in other e-business services. Advertisement providers pay part or the entire service cost; hence, users enjoy services at a low price or free of charge. However, users perceive advertisements as clutter and avoid them. To reduce advertising avoidance and enhance advertising efficiency, various methods, such as raising advertising relevance, seeking advertisement permission, or changing advertisement position, are suggested. This study introduces L-Shape advertising, a new strategy suitable for mobile video streaming services, as it is less distractive and effective. We demonstrate the superiority of L-Shape advertising compared to popular advertising formats such as skippable and non-skippable advertisements. The study empirically tests how the proposed strategy performs and examines users’ cognitive processes applying eye-tracking technology. We found that L-Shape advertising is less distractive but still effective.     

基于小波变换的MPEG视频流预测模型

提出了一种基于小波变换的MPEG视频流的预测模型,该模型对原始数据做Haar小波变换以后,充分考虑尺度系数和乘子的不同统计特性,对尺度系数和乘子采用不同的预测模型分别预测,最终通过多重分形小波模型建模过程合成MPEG视频流序列。利用仿真和数学分析的方法对预测模型在刻画MPEG视频流的主要统计特性方面进行性能分析,从误差、数据的概率分布特性以及自相似特性等方面,证实了该预测模型的优越性能。     

Energy optimization for latency- and quality-constrained video applications

The trade-off between energy consumption and media quality is a fundamental design issue in mobile multimedia. This energy optimization technique based on frame skipping and buffering exploits the characteristics of video applications, particularly their tolerance to variation in latency and video quality to increase slack time and its use in dynamic voltage scaling. The technique adapts the frame rate to the degree of motion activity by skipping redundant frames. Thus, it intentionally creates slack time as long as video quality is not unacceptably degraded. We implemented this technique with only a slight modification of the original encoder algorithm and no modification to the decoder.     

Content-aware rate allocation for efficient video streaming via dynamic network utility maximization

Nowadays it is vital to design robust mechanisms to provide QoS for multimedia applications as an integral part of the network traffic. The main goal of this paper is to provide an efficient rate control scheme to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in congested networks. Towards this, we introduce a content-aware time-varying utility function, in which the quality impact of video content is incorporated into its mathematical expression. Moreover, we analytically model the buffer requirements of video sources in two ways: first as constraints of the optimization problem to guarantee a minimum rate demand for each source, and second as a penalty function embedded as part of the objective function attempting to achieve the highest possible rate for each source. Then, using the proposed analytical model, we formulate a dynamic network utility maximization problem, which aims to maximize the aggregate hybrid objective function of sources subject to capacity and buffer constraints. Finally, using primal–dual method, we solve DNUM problem and propose a distributed algorithm called CA-DNUM that optimally allocates the shared bandwidth to video streams. The experimental results demonstrate the efficacy and performance improvement of the proposed content-aware rate allocation algorithm for video sources in different scenarios.     

Performance analysis of Energy Efficient Ethernet on video streaming servers

Current trends on traffic growth oversee a steady increase of video streaming services, and the subsequent development of the associated infrastructure to allocate and distribute such contents. One of the operational costs associated to this infrastructure is the power bill. Therefore any mechanism used to decrease it, reducing also the carbon footprint associated to it, is welcome. In this work we investigate the suitability of the recently standardized IEEE 802.3az Energy Efficient Ethernet (EEE) for video traffic generated by video-streaming servers. The conclusion of the analysis is positive about the achievable energy savings, due to the inherent features of traffic patterns of video-streaming servers which help reducing the number of transitions between active and low-power modes in EEE.     

SVCEval-RA: an evaluation framework for adaptive scalable video streaming

Multimedia content adaption strategies are becoming increasingly important for effective video streaming over the actual heterogeneous networks. Thus, evaluation frameworks for adaptive video play an important role in the designing and deploying process of adaptive multimedia streaming systems. This paper describes a novel simulation framework for rate-adaptive video transmission using the Scalable Video Coding standard (H.264/SVC). Our approach uses feedback information about the available bandwidth to allow the video source to select the most suitable combination of SVC layers for the transmission of a video sequence. The proposed solution has been integrated into the network simulator NS-2 in order to support realistic network simulations. To demonstrate the usefulness of the proposed solution we perform a simulation study where a video sequence was transmitted over a three network scenarios. The experimental results show that the Adaptive SVC scheme implemented in our framework provides an efficient alternative that helps to avoid an increase in the network congestion in resource-constrained networks. Improvements in video quality, in terms of PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) are also obtained.     

Adaptive hybrid error correction model for video streaming over wireless networks

The Hybrid ARQ (HARQ) mechanism is the well-known error packet recovery solution composed of the Automation Repeat reQuest (ARQ) mechanism and the Forward Error Correction (FEC) mechanism. However, the HARQ mechanism neither retransmits the packet to the receiver in time when the packet cannot be recovered by the FEC scheme nor dynamically adjusts the number of FEC redundant packets according to network conditions. In this paper, the Adaptive Hybrid Error Correction Model (AHECM) is proposed to improve the HARQ mechanism. The AHECM can limit the packet retransmission delay to the most tolerable end-to-end delay. Besides, the AHECM can find the appropriate FEC parameter to avoid network congestion and reduce the number of FEC redundant packets by predicting the effective packet loss rate. Meanwhile, when the end-to-end delay requirement can be met, the AHECM will only retransmit the necessary number of redundant FEC packets to receiver in comparison with legacy HARQ mechanisms. Furthermore, the AHECM can use an Unequal Error Protection to protect important multimedia frames against channel errors of wireless networks. Besides, the AHECM uses the Markov model to estimate the burst bit error condition over wireless networks. The AHECM is evaluated by several metrics such as the effective packet loss rate, the error recovery efficiency, the decodable frame rate, and the peak signal to noise ratio to verify the efficiency in delivering video streaming over wireless networks.     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.     

面向视频流媒体的应用层多播技术研究 *

主要关注面向视频流媒体的应用层多播（ALM）技术的研究近况。首先总结了ALM的研究概况和分 类,重点研究了几个针对视频流媒体应用而提出的ALM协议和框架,讨论其中用到的一些前沿技术,分析这些 协议的优缺点,同时给出一些改进思路。最后展望了ALM视频流媒体技术的未来研究方向。     

移动视频体验质量的量化评价模型

移动视频体验质量（QoE）的研究正变得日益重要,现有的QoE评价模型缺少相应的量化方法。结合移动视频业务的特点,提出了两层权值的QoE量化评价模型,即QoE由六种“影响层面”按各自的一级权值叠加而成;进一步地,每个“影响层面”又由多个“影响因素”构成。除了丢包和抖动两个“影响因素”外,其他“影响因素”均具有独立的二级权值。提出通过层次分析法（AHP）计算出所有的一级权值,并且进一步计算出除了丢包、抖动外的其他二级权值。由于丢包和抖动这两个影响因素相互依赖,无法计算各自的二级权值,因此通过网络模拟实验,找出两者的双变量函数f（丢包,抖动）,由此得到完整的两层权值QoE量化评价模型。所提QoE量化评价模型通过测评人员的QoE评分进行验证,实验结果表明该模型获得了较高的准确度,证明了上述模型量化方法的可靠性。     

基于流媒体的移动视频直播系统的设计与实现

视频直播是最能体现媒体信息实时传播的媒体形态,介绍了一个利用3G/4G手机进行视频拍摄和传输的移动直播系统。分析了当今移动直播系统的发展趋势,介绍了如何设计并实现一个基于流媒体技术的移动视频直播系统。实际测试结果表明,用户可以随时随地地利用3G/4G的iPhone手机进行直播,观众可以在远程PC端利用VLC播放器进行观看,提高直播系统的便捷性。     

移动流媒体视频监控系统的研究与设计*

设计了一套基于3GPP标准的移动流媒体视频实时监控系统,同时介绍了系统采用的服务器集群、安全防盗链等关键技术,最后给出了系统主要功能参数的评测。