Packet loss resilience using unequal forward error correction assignment for video transmission over communication networks

Forward error correction (FEC) methods have been developed for packet loss resilience in application layer for real-time video transmission over communication networks. In this paper, an efficient packet loss resilience method is proposed using closed form solution for unequal FEC assignment based on a new packet distortion model. We first derive the packet distortion model by investigating the error concealment property and error propagation effect in H.264. To select the source and channel rate minimizing the overall distortion, we present a model-based rate allocation algorithm using the packet distortion model and rate-distortion function. Then we propose the closed form solution for unequal FEC assignment, which uses the packet distortion model and considers channel status information. Simulation results show that the proposed method gives substantial improvement for the received video quality in packet-lossy Internet and wireless network environments, while it requires much less computational complexity compared to the previous scheme.     

基于IP无线网络FGS视频传输的多乘积码方案

研究基于IP无线网络中精细粒度可伸缩性(FGS)视频的传输。基于包交换的IP无线网络通常由两段链路组成:有线链路和无线链路。为了处理这种混合网络中不同类型数据包的丢失情况,对FGS视频增强层数据运用了一个具有比特平面间不平等差错保护(BPUEP)的多乘积码前向纠错(MPFEC)方案进行信道编码。对FGS增强层每一个比特平面(BP),在传输层,采用里德—索罗蒙码(RS)提供比特平面间的保护;而在链路层,则运用循环冗余校验码(CRC)串联率兼容穿孔卷积码(RCPC)提供数据包内保护。还提出了一个率失真优化的信源—信道联合编码的码率配置方案,仿真结果显示出该方案在提高接收端视频质量方面的优势。     

信源-信道联合编码的FGS增强层视频传输研究

研究了基于IP无线网络中精细粒度可伸缩性（FGS）视频的侍输。基于包交换的IP无线网络通常由两段链路组成：有线链路和无线链路。为了处理这种混合网络中不同类型数据包的丢失情况,对FGS视频增强层数据运用了一个具有比特平面间不平等差错保护（BPUEP）的多乘积码前向纠错（MPFEC）方案进行信道编码。对FGS增强层每一个比特平面（BP）,在传输层,采用里德——索罗蒙码（RS）提供比特平面间的保护;而在链路层,则运用循环冗余校验码（CRC）串联率兼容穿孔卷积码（RCPC）提供数据包内保护。还提出了一个率失真优化的信源——信道联合编码的码率配置方案,仿真结果显示出该方案在提高接收端视频质量方面的优势。     

基于自适应遗传算法的SVC非均等错误保护算法

为提高可伸缩视频编码(SVC)在丢包的网络传输环境下的抗误码性能,提出了一种基于自适应遗传算法的SVC非均等错误保护算法。首先针对可伸缩视频编码的网络抽象层单元数据包头的特点,设计了一种新的网络抽象层单元的封装方案。然后将前向纠错编码的校验位在各层的分配转化为多约束条件下的优化问题,再引入惩罚函数将多约束优化问题转化为无约束优化问题,进而采用自适应遗传算法进行求解。仿真实验结果表明,与目前典型的非均等错误保护算法相比,该算法使重建的可伸缩视频编码的峰值信噪比的平均值提高了0.8dB~1.95dB,并有效提高了可伸缩视频编码在接收端的解码速度和重建质量。     

基于错误传播模型的非均等视频流丢失保护

提出一种适用于丢包网络、面向图像组（GOP）层的非均等视频流丢失保护方案。利用GOP中不同帧之间的非均等显著性,将不同数量前向错误校正包分配到GOP层的不同帧中。采用帧间包交错机制将突发包丢失分散到不同帧上,提高处理突发包丢失时的鲁棒性。仿真结果表明,在不同信道丢失模式下,该方案能提高视频接收质量。     

立体视频传输中右通道整帧丢失的错误掩盖算法

当立体视频流通过网络传输时,由于网络拥塞等造成的数据包丢失常常会引起整个视频帧丢失.鉴于此,本文基于H.264/AVC视频编码标准提出了两种立体视频整帧错误掩盖方案,它们分别利用立体视频序列的不同特点来进行恢复.结果显示,利用这两种算法恢复的图像均能获得很好的主客观质量.     

一种基于H.264的有效视频抗误码算法

由于实时视频数据流在存储或传输中的错误、丢包等原因，解码器接收到的数据流可能不完整，无法正常解码，错误隐藏是解决这个问题的一种有效方法，在对视频压缩标准H．264研究的基础上，提出了一种基于运动跟踪的快速交互式抗误码算法；该算法由解码器检测定位出误码位置，然后编码器在编码后续帧时采用运动跟踪的原理定位出受误码影响区域，并对区域内的宏块数据进行相应的处理，以防止误码进一步扩散；仿真结果表明，提出的交互式视频抗误码方法能够快速有效地抑制误码的扩散，保证恢复视频质量。     

一种自适应的视频流化前向纠错算法

网络视频应用经常会受到数据包丢失或错误以及网络带宽资源不足的干扰.相关研究表明:在多数情况下,动态变化的网络带宽和丢包率是影响视频流化质量的关键因素.因此,为了保证视频质量,可以采用前向纠错(forward error correction,简称FEC)编码来提高视频数据传输的可靠性;同时,为了适应网络状态的变化,发送端可以调节视频数据的发送速率,并在视频源数据与FEC数据之间合理分配网络传输带宽.首先通过对视频流结构的分析,在充分考虑帧之间的依赖关系和帧类型的基础上提出了一种帧的解码模型.在此基础上,建立了用于在视频源数据和FEC数据之间分配网络带宽资源的优化算法.实验表明,该模型可以有效地适应网络状态的变化,并通过优化分配网络带宽资源来使接收端获得最大的可播放帧率.     

Sub-packet forward error correction mechanism for video streaming over wireless networks

Traditional Forward Error Correction (FEC) mechanisms can be divided into Packet level FEC (PFEC) mechanisms and Byte level FEC (BFEC) mechanisms. The PFEC mechanism of recovering from errors in a source packet requires an entire FEC redundant packet even though the error involves a few bit errors. The recovery capability of the BFEC mechanism is only half of the FEC redundancy. Accordingly, an adaptive Sub-Packet FEC (SPFEC) mechanism is proposed in this paper to improve the quality of video streaming data over wireless networks, simultaneously enhancing the recovery performance and reducing the end-to-end delay jitter. The SPFEC mechanism divides a packet into n sub-packets by means of the concept of a virtual packet. The SPFEC mechanism uses a checksum in each sub-packet to identify the position of the error sub-packet. Simulation experiments show the adaptive SPFEC mechanism achieves high recovery performance and low end-to-end delay jitter. The SPFEC mechanism outperforms traditional FEC mechanism in terms of packet loss rate and video Peak Signal-to-Noise Ratio (PSNR). SPFEC offers an alternative for improved efficiency video streaming that will be of interest to the designers of the next generation environments.     

基于RCPC-FEC的3G视频传输模型研究

为了有效克服3G无线网络传输中的比特错误和数据包丢失问题,采用码率兼容删除卷积码（RCPC）抑制比特错误问题,通过前向纠错（FEC）减小数据丢包率,将RCPC与FEC有机结合提出了一种基于RCPC-FEC的无线网络视频传输模型。在3G无线网络传输的不同比特错误率和数据丢包率下对该模型进行了测试,实验结果证明了模型的有效性和可行性。     

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.     

Unequal error protection under bitrate constraint for video streaming over internet

This article describes a simple packet-level FEC system suitable for unequal error protection of layered video streams, that we called TAPIOCA (in French, Transport Audiovisuel avec Protection Inégale des Objets et Contrôle d’Admission). It is designed in a way that the FEC overhead induced by redundant packets is perfectly controlled by the sender. In order to achieve that, TAPIOCA calculates on-the-fly the optimal erasure code to be used, video data unit by video data unit, under a given bitrate constraint. In addition, and contrary to the well-known PET (Priority Encoding Transmission) system, the video data units of each layer are encoded separately. This is especially useful when all layers are not output from the video coder at the same time. Simulation results for MPEG-4 video streaming show that the proposed FEC system can be very efficient even if packet losses are due to network congestion. Moreover, comparison with PET system shows that TAPIOCA exhibits better performance, considering criteria including the decodable frame rate, protection system efficiency and computational cost.     

实时视频通信中的自适应前向纠错方案设计

本文针对实时视频通信中的网络丢包问题,提出了一种基于Reed Solomon算法的自适应FEC方案。与以往的静态FEC编解码方案不同,该方案引入一种新的基于SIP／RTP的QoS反馈机制,根据丢包率大小在发送端调整FEC冗余度、整体发送速率以及封包大小来保证服务质量;并针对网络突发丢包情况,在对数据包进行FEC编码时采用了交织技术     

Concurrent multipath transmission combining forward error correction and path interleaving for video streaming

Video streaming is a popular application on next generation networks (NGNs). However, video streaming over NGNs has many challenges due to the high bit error rates of these networks. Forward error correction (FEC) is often applied to improve the quality of video streaming. However, continuous lost packets decrease the recovery performance of FEC protection in NGNs. To disperse continuous lost packets to different FEC blocks, we propose a concurrent multipath transmission that combines FEC with path interleaving. Our proposed control scheme adaptively adjusts the FEC block length and concurrently sends data interleaved over multiple paths. Experimental results with our approach show improved packet loss and signal to noise ratio performance.     

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.     

Scalable Video Multicast Using Expanding Window Fountain Codes

Fountain codes were introduced as an efficient and universal forward error correction (FEC) solution for data multicast over lossy packet networks. They have recently been proposed for large scale multimedia content delivery in practical multimedia distribution systems. However, standard fountain codes, such as LT or Raptor codes, are not designed to meet unequal error protection (UEP) requirements typical in real-time scalable video multicast applications. In this paper, we propose recently introduced UEP expanding window fountain (EWF) codes as a flexible and efficient solution for real-time scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be ldquotunedrdquo at the server side to meet the different reception criteria of heterogeneous receivers. The performance analysis using both analytical results and simulation experiments of H.264 scalable video coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.     

Cross-Layer Error Control for Multimedia Streaming in Wireless/Wireline Packet Networks

In this paper, we propose a cross-layer error control framework for robust and low delay multimedia streaming in tandem-connected IEEE 802.11 wireless LANs and the Internet. For this network configuration, we model the end-to-end delay and packet loss rate as a function of the automatic repeat request (ARQ) and forward error correction (FEC) error control mechanisms that are employed at the application and wireless link layers. The analytical model is used as the basis of a delay-constrained error control algorithm that adapts the protection level at the application and link layers so that the end-to-end packet loss rate is minimized. With extensive simulations, we validate the efficiency of the proposed cross-layer error control methodology for delay-sensitive pre-compressed video streaming.      

Error sensitivity model based on spatial and temporal features

Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.

     

基于MPEG 4的无线视频传输方案的研究*

提出一种基于MPEG4的无线视频传输非等重误码保护方案。根据信道反馈的丢包率信息,自适应选择输出视频流的保护模式。在丢包信道中,对MPEG4码流中运动信息和纹理信息采取不同的保护力度,使用FEC重点保护运动信息数据提高其对误码的鲁棒性。在无丢包信道中,不对数据进行保护,降低打包开销,减少冗余数据。     

一种适用于H.264的时域差错掩盖改进算法

视频压缩码流在信道传输时 ,由于受到信道带宽或者稳定性的影响 ,容易发生数据的损坏或者丢失 ,这样不仅会对当前的视频帧产生影响 ,而且差错会延续到随后的视频帧 ,因此 ,需要采用某种技术来降低差错的影响。针对这一问题 ,在对最新视频压缩标准 H.2 6 4研究的基础上 ,基于 H.2 6 4标准的框架 ,对已有的差错掩盖算法进行了改进 ,提出了适合 H.2 6 4编码标准的时域子块匹配差错掩盖算法。该算法首先采用 8× 8的子块代替 16× 16的宏块 ,作为差错掩盖的运算单元 ,然后对不同的子块采用不同的边界像素 ,利用边界匹配算法 ,并通过改进的 1/ 4像素精度菱形搜索法在参考帧内找到最佳匹配块。实验结果证明 ,由于该算法有效地利用了 H.2 6 4压缩码流里的信息 ,因此 ,同传统的时域差错掩盖算法相比 ,对差错信号有更好的恢复效果。