WMSN中的分布式视频编码GOP大小控制方法

针对分布式视频编码（DVC）序列在无线多媒体传感器网络（WMSN）中传输可靠性较低的问题,提出了一种考虑节点能耗的图像组（GOP）大小控制方法。该方法分析了DVC编码框架及节点的能量消耗情况,定义了节点的可用度和帧的活动度,并对分布式视频编码序列在编码和传输过程的能耗进行了计算。在此基础上,综合考虑帧的活动度与节点能耗,实现了对分布式视频编码GOP大小的有效控制。仿真实验结果表明,与现有的GOP大小控制方法相比,该方法提高了无线多媒体传感器网络中的DVC序列的峰值信噪比,降低了节点的平均能耗,其传输可靠性也得到了显著改善。     

基于可伸缩视频编码的率失真优化编码算法*

可伸缩视频编码（SVC）可实现视频流空间、时间和信噪比的完全伸缩,但在差错信道上传输容易引起误差扩散,为此提出一种基于SVC的率失真优化编码算法。该算法在分析了差错信道下传输可伸缩视频流误差扩散失真的基础上,在率失真优化模型中引入时间和信噪比分级的误差分级参数,并根据信道状态自适应地确定宏块编码模式。该算法能够有效地抑制误码在各分层的扩散,提高了可伸缩视频流的鲁棒性。仿真结果表明,算法与以往的算法相比具有更好的抗误码性能,适合视频数据在差错信道上的传输。     

能量受限条件下率失真性能优化机制研究

视频编码率失真性能的研究一般没有考虑到能量对率失真(RD)性能的影响。本文针对WSNs节点如何分配计算能耗与传输能耗,使得视频信号RD性能最好的问题展开了研究。首先建立了压缩与传输部分的功耗-率-失真模型,并由此得到系统的功耗-率-失真模型。随后通过数值方法,分析了压缩功耗与系统失真之间关系。实验结果表明存在一个最优的压缩功耗PS值,使得系统的失真度最小。     

基于失真估计和码率控制的视频传输新方案

针对差错信道上的实时视频编码和传输问题,提出一种基于逐帧失真估计和码率控制的新型时域不等差错保护方案。该方案给出一种低复杂度逐帧传输失真估计模型,估计差错信道下的视频传输失真,结合码率控制算法,通过对编码量化参数的简单搜索,逐帧求出最优的压缩编码和信道保护策略。仿真结果表明,与现有方案相比,该方案不但延迟小、复杂度适中,而且能提高重建视频序列的平均质量,减少逐帧之间的质量波动。     

一种基于缓存的SVC流媒体质量自适应算法

新型视频编码标准H.264/AVC的扩展SVC(可扩展视频编码)技术为未来异构环境下的流媒体传输提供了一种新的解决方案。针对基于SVC的流媒体系统中的核心问题——质量自适应,提出了一种新的算法。该算法分为预缓存和基于缓存的动态质量自适应两个阶段,不但能够自适应网络带宽的动态变化,而且避免了频繁的质量切换,使得用户能够在动态的网络环境下获得尽可能好的主观观看体验。实现了一套SVC流媒体原型系统,并在此基础上验证了算法的有效性。     

基于视频通信的信道码码率分配方法

针对差错信道下视频信源的不等差错保护问题,设计一个新的信道码码率分配策略,对一个图片组中不同位置的帧给予不同强度的保护。该策略考虑了视频信源的误码扩散问题,分析了差错掩盖方法与信道失真计算的关系,通过在编码端有效估计信道差错所引入的失真并建立最优码率分配问题,利用遗传算法快速得到最优的信道码码率分配方案。实验采用H.264视频编码标准,结果证明该策略能较好地提升视频传输系统性能。     

无线视频传感器网络的能量优化

为了解决无线传感器网络节点计算能力和节点能量问题,本文从非能量约束下的视频传输入手,了解视频传输的码率—失真模型,获取编码性能与失真的定量关系。建立无线传感器的单节点的＂能量—码率—失真＂（Power—Rate—Distortion）的优化模型,使无线传感器网络在一定失真允许的范围内,单节点能量消耗最优。利用建立的优化模型,在一定失真允许的情况下,可以得到最优的模块间的能量分配方案。     

基于H.264 SVC的IP网络视频传输系统的实现

可扩展视频编码已成为国际上视频技术研究的热点之一,联合视频组(JVT)也已标准化了H.264视频编码标准的扩展可扩展视频编码(H.264 SVC)。首先概述了H.264 SVC的基本编码框架,在此基础上,设计了一套基于H.264 SVC的IP网络视频传输系统。该系统实现了视频流在IP网络上的可靠传输,并且满足了不同用户对空间分辨率、帧率和视频质量的个性化需求等。     

基于3G网络的视频传输策略的研究

文章主要论述了基于3G无线网络上提高QoS的视频传输策略。综合考虑时变无线信道状况和视频编码特性,实现基于最小失真的联合信源和信道编码。首先,构建了基于3G无线网络的端-端视频传输架构。其次,提出了基于动态监测和实时测量网络吞吐量,自定义高层协议机制和基于UEP(不对等错误保护)的打包策略,通过在3G无线网络上吞吐量和误码率的测量来评价时变无线网络的信道/网络资源。再次,错误弹性机制使得端-端视频传输的QoS达到全局最优。仿真测试结果表明本文的策略可以提高视频传输的鲁棒性。     

无线多媒体传感器网络图像通信性能分析

对无线视频传感器节点的图像通信活动进行研究,分析了其在资源受限的无线多媒体传感器网络中进行图像编码和传输的性能。对基于DCT和DWT的图像编码与传输的能量消耗和率失真进行建模分析。根据所建立的能量消耗与率失真模型,对有限的能量和传输带宽进行优化分配。根据监测场景的统计信息,调整编码中所使用的参数,以使得能量消耗最小化。仿真实验结果表明,所提出的方法在保证应用所需图像质量的情况,有效地降低了无线传感器节点的数据通信量和计算过程的能耗。     

A novel hybrid 3D video service algorithm based on scalable video coding (SVC) technology

A scalable video coding (SVC) server can simultaneously provide a single bitstream with a fixed maximum service layer for different kinds of devices having different memory capacity, network bandwidth, and CPU performance requirements. An efficient hybrid 3D video service scheme is proposed without violation of the SVC standard technology for multiple transmission paths. A dynamic local disparity vector estimation algorithm is used to reflect the motion shift component between stereo views in the inter-layer prediction stage of the SVC encoder. To improve the coding efficiency, an adaptive search scheme based on distortion rates (DRs) between corresponding and reference macroblocks is used. Based on experimental results, up to 1.41 dB of quality improvement using JSVM 9.19 reference software is verified.     

面向多媒体传感器网络的视频压缩新算法

视频压缩可以降低多媒体传感器网络中的数据信息量。传统分布式视频编码方案采用区域统一编码,可能导致运动剧烈区域解码估计的失真。提出一种改进的分布式视频编码算法。该算法基于图像梯度场,针对Wyner-Ziv帧不能准确编解码的区域,通过ROI判定准则提取该区域并基于熵编码压缩,图像其他区域则基于LDPC实现分布式编解码,进而实现视频的优化传输。仿真实验表明：本文算法可增强运动剧烈区域的编码效率,在降低码率的同时提高解码图像质量,最终降低传感器节点能耗。     

Low-Delay Low-Complexity Bandwidth-Constrained Wireless Video Transmission Using SVC Over MIMO Systems

We propose an efficient strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec (SVC), which provides combined temporal, quality and spatial scalability. At the transmitter, we estimate the decoded video distortion for given channel conditions taking into account the effects of quantization, packet loss and error concealment. The proposed scalable decoder distortion algorithm offers low delay and low complexity. The performance of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. The bandwidth constrained allocation problem considered here is simplified and solved for one O-STBC symbol at a time. Furthermore, we take the advantage of the hierarchical structure of SVC to attain the optimal solution for each group of pictures (GOP) of the video sequence. We incorporate the estimated decoder distortion to optimally select the application layer parameter, i.e., quantization parameter (QP), and physical layer parameters, i.e., channel coding rate and modulation type for wireless video transmission.      

Content-aware optimization on rate-distortion and network traffic for scalable video multicast networks

This paper aims to optimize the content-aware prioritization of scalable video multicast, which is coupled with multipath streaming and network coding based routing. It constructs multiple layer distribution meshes for the scalable video stream to minimize the total video distortion at all the receivers, determines the base layer meshes with minimum costs to maintain application-layer QoS and the layer synchronization of SVC streaming, and improves the network throughput by encouraging path-overlapping transmissions and thus allowing bandwidth sharing among different receivers for the same video layer by utilizing network coding. The targeted problem is formulated into a minimization programming in which the quality variation between layers, the transmission cost of the base layer, as well as the efficient resource utilization are jointly considered. By decomposition and dual approach, the global convex problem is solved by a two-level decentralized iterative algorithm. The implementation of the distributed algorithm is discussed with regard to the communication overhead, and the convergence performance is validated by numerical experiments. Packet-level simulations demonstrate that the proposed algorithm could approximately achieve the maximum flow rates determined by Max-Flow Min-Cut Theorem and benefit the overall received video quality.     

一种用于无线局域网可伸缩视频传输的跨层结构与SBED算法*

为了增强802.11e无线局域网上的视频传输质量,提出了一种跨层结构与自适应映射算法。将H.264可伸缩视频编码（SVC）的层次信息与802.11e MAC层的访问类（AC）相结合,基于重要性早期检测（SBED）策略将SVC数据包动态映射到合适的AC上,在基础层损失率与平均重要性损失度间实现良好的平衡。仿真表明,该方案的PSNR性能明显优于现有的静态与随机映射方案。     

Adaptive interface selection over cloud-based split-layer video streaming via multi-wireless networks

As mobile devices such as tablet PCs and smartphones proliferate, the online video consumption over a wireless network has been accelerated. From this phenomenon, there are several challenges to provide the video streaming service more efficiently and stably in the heterogeneous mobile environment. In order to guarantee the QoS of real-time HD video services, the steady and reliable wireless mesh is necessary. Furthermore, the video service providers have to maintain the QoS by provisioning streaming servers to respond the clients’ request of different video resolution. In this paper, we propose a reliable cloud-based video delivery scheme with the split-layer SVC encoding and real-time adaptive multi-interface selection over LTE and WiFi links. A split-layer video streaming can effectively scale to manage the required channels on each layer of various client connections. Moreover, split-layer SVC model brings streaming service providers a remarkable opportunity to stream video over multiple interfaces (e.g. WiFi, LTE, etc.) with a separate controlling based on their network status. Through the adaptive interface selection, the proposed system aims to ensure the maximizing video quality which the bandwidth of LTE/WiFi accommodates. In addition, the system offers cost-effective streaming to mobile clients by saving the LTE data consumption. In our system, an adaptive interface selection is developed with two different algorithms, such as INSTANT and EWMA methods. We implemented a prototype of mobile client based on iOS particularly by using iPhone5S. Moreover, we also employ the split-layer SVC encodes in streaming server-side as the add-on module to SVC reference encoding tool in a virtualized environment of KVM hypervisor. We evaluated the proposed system in an emulated and a real-world heterogeneous wireless network environments. The results show that the proposed system not only achieves to guarantee the highest quality of video frames via WiFi and LTE simultaneous connection, but also efficiently saves LTE bandwidth consumption for cost-effectiveness to client-side. Our proposed method provides the highest video quality without deadline misses, while it consumes 50.6% LTE bandwidth of ‘LTE-only’ method and 72.8% of the conventional (non-split) SVC streaming over a real-world mobile environment.     

基于视频业务质量优化的认知无线电传输信道选择

认知无线电网络中,次级用户选择信道的传统技术基于信道特性对传输信道进行随机选择,忽略了应用层视频业务对信道质量的要求。针对该问题提出了一种基于视频业务质量优化的信道选择技术,优化视频业务端到端的传输质量。通过最小化端到端视频失真,跨层优化综合选择物理层传输信道、自适应调制与编码模式以及应用层的编码量化参数。该方法在多信道认知无线电网络下进行了大量的视频传输仿真模拟实验。实验结果表明该方法能够比不含信道选择的跨层优化方法提高认知无线电网络下次级用户的视频传输业务客观质量1.5 dB以上。