Video transcoding for adaptive bitrate streaming over edge-cloud continuum

Video transcoding is to create multiple representations of a video for content adaptation.It is deemed as a core technique in Adaptive BitRate(ABR)streaming.How to manage video transcoding affects the performance of ABR streaming in various aspects,including operational cost,streaming delays,Quality of Experience(QoE),etc.Therefore,the problems of implementing video transcoding in ABR streaming must be systematically studied to improve the overall performance of the streaming services.These problems become more worthy of investigation with the emergence of the edge-cloud continuum,which makes the resource allocation for video transcoding more complicated.To this end,this paper provides an investigation of the main technical problems related to video transcoding in ABR streaming,including designing a rate profile for video transcoding,providing resources for video transcoding in clouds,and caching multi-bitrate video contents in networks,etc.We analyze these problems from the perspective of resource allocation in the edge-cloud continuum and cast them into resource and Quality of Service(QoS)optimization problems.The goal is to minimize resource consumption while guaranteeing the QoS for ABR streaming.We also discuss some promising research directions for the ABR streaming services.

MEC-Assisted Flexible Transcoding Strategy for Adaptive Bitrate Video Streaming in Small Cell Networks

Adaptive bitrate video streaming(ABR)has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user preferences.However,there are still many problems in achieving high-quality ABR video streaming over cellular networks.Mobile Edge Computing(MEC)is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network(RAN).In this paper,we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage,computing,and spectrum resources.According to the information collected from users,the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement strategy.Specifically,we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control,resource allocation,and user preferences.We formulate this problem as a nonconvex optimization and mixed combinatorial problem.Moreover,the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.

Scheduling Heuristics for Live Video Transcoding on Cloud Edges

Efficient video delivery involves the transcoding of the original sequence into various resolutions,bitrates and standards,in order to match viewers’capabilities.Since video coding and transcoding are computationally demanding,performing a portion of these tasks at the network edges promises to decrease both the workload and network traffic towards the data centers of media providers.Motivated by the increasing popularity of live casting on social media platforms,in this paper we focus on the case of live video transcoding.Specifically,we investigate scheduling heuristics that decide on which jobs should be assigned to an edge minidatacenter and which to a backend datacenter.Through simulation experiments with different Qo S requirements we conclude on the best alternative.

MPEG-2到H.263 Transcoding的若干关键问题

该文在前人的工作基础上提出了在压缩域MPEG-2到H.263码流transcoding的通用框架,并进行了深入的分析和优化。这种优化的目标是,在实现视频流格式转换和瘦化的同时能够最大程度地利用源视频流的计算信息。该文对transcoding过程进行了形式化推导和分析,使transcoding的计算复杂性和存储消耗大大降低,同时理论上能实现在transcoding级上的更好视频质量(与传统方法相比)。这种通用框架能够以标准附件的形式加入高级应用功能,如数据隐藏模块、误码掩藏模块、链路适应模块。这种工作的意义在于,通过把transcoding复杂性推向网络边缘设备transcoder,降低了用户的硬件更新成本和视频服务器存储成本,使不同类型终端和不同链路环境的用户能够高效地透明地访问高质量视频流域。同时,计算复杂性和存储消耗的降低有利于服务器端以较低成本实现更多用户接入能力。

Efficient video downscaling transcoder from MPEG-2 to H.264

The new H.264 video coding standard achieves significantly higher compression performance than MPEG-2. As the MPEG-2 is popular in digital TV, DVD, etc., bandwidth or memory space can be saved by transcoding those streams into H.264 in these applications. Unfortunately, the huge complexity keeps transcoding from being widely used in practical applications. This paper proposes an efficient transcoding architecture with a smart downscaling decoder and a fast mode decision algorithm. Using the proposed architecture, huge buffering memory space is saved and the transcoding complexity is reduced. Performance of the proposed fast mode decision algorithm is validated by experiments.

基于分布式转码的多路数字电视节目处理系统设计与实现

以湖北省地面数字电视省级播控平台多画面模块为对象,介绍基于分布式转码技术处理多路地面数字电视节目的解决方案。方案采用纯软件方法将多节目流分解成单节目流,再对单节目流进行推流、转码、转封装,最终实现在一个页面中播放一个地面数字发射台发射的所有电视节目。该方案具有高灵活性和低硬件成本的特点。

Sports video summarization and adaptation for application in mobile communication

Sports video appeals to large audiences due to its high commercial potentials. Automatically extracting useful semantic information and generating highlight summary from sports video to facilitate users' accessing requirements is an important problem, especially in the forthcoming broadband mobile communication and the need for users to access their multimedia information of interest from anywhere at anytime with their most convenient digital equipments. A system to generate highlight summaries oriented for mobile applications is introduced, which includes highlight extraction and video adaptation. In this system, several highlight extraction techniques are provided for field sports video and racket sports video by using multi-modal information. To enhance users' viewing experience and save bandwidth, 3D animation from highlight segment is also generated. As an important procedure to make video analysis results universally applicable, video transcoding techniques are applied to adapt the video for mobile communication environment and user preference. Experimental results are encouraging and show the advantage and feasibility of the system for multimedia content personalization, enhancement and adaptation to meet different user preference and network/device requirements.

Intelligent Mobile Video Surveillance System with Multilevel Distillation

This paper proposes a mobile video surveillance system consisting of intelligent video analysis and mobile communication networking. This multilevel distillation approach helps mobile users monitor tremendous surveillance videos on demand through video streaming over mobile communication networks. The intelligent video analysis includes moving object detection/tracking and key frame selection which can browse useful video clips. The communication networking services, comprising video transcoding, multimedia messaging, and mobile video streaming, transmit surveillance information into mobile appliances. Moving object detection is achieved by background subtraction and particle filter tracking. Key frame selection, which aims to deliver an alarm to a mobile client using multimedia messaging service accompanied with an extracted clear frame, is reached by devising a weighted importance criterion considering object clarity and face appearance. Besides, a spatial- domain cascaded transcoder is developed to convert the filtered image sequence of detected objects into the mobile video streaming format. Experimental results show that the system can successfully detect all events of moving objects for a complex surveillance scene, choose very appropriate key frames for users, and transcode the images with a high power signal-to-noise ratio （PSNR）.

FSpot:Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances

HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing network channels,but encoding videos into multiple representations with selected encoding parameters is a significant challenge.However,video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds.In turn,the public clouds,such as Amazon elastic compute cloud(EC2),provide hundreds of computing instances optimized for different purposes and clients’budgets.Thus,there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations.Additionally,the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content.In this paper,we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple×264 codec encoding parameters and video sequences of varying complexity.Then,we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs.Furthermore,we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost.The results show that our approach,on average,can reduce the encoding costs by at least 15.8%and up to 47.8%when compared to a random selection of EC2 spot instances.

基于关键帧的多媒体视频无损转码压缩算法

传统多媒体视频压缩算法无法准确提取视频转码关键帧,导致视频转码压缩的信噪比与压缩比均偏低。为此提出基于关键帧的多媒体视频无损转码压缩算法。通过视频图像包含的I帧分量信息构建DC缩图。利用相似性度量图像帧中所处不同位置的信息重要性差值,提取关键帧,采取自适应运动补偿方式获取各关键帧的预测残差图和运动向量。采用SPIHT算法量化小波系数及分层编码对其处理,实现多媒体视频无损转码压缩。实验结果表明,上述算法具有较好的查全率、查准率和压缩比,且在不同带宽和分量条件下能够保持较高的信噪比,保证了压缩后的多媒体视频具有良好的清晰度。

视频转码技术回顾

随着各种视频压缩标准的制定及计算机硬件、网络技术的发展,视频信息的应用呈现出爆炸式的增长。为了适应用户终端的多样性及网络自身的传输特性,20世纪90年代后期,视频转码成为视频编码领域一个新的研究热点。本文从视频转码的应用、体系结构和关键技术三个方面,回顾近几年来在视频转码方面的研究进展。

基于分布式监控数字单元系列的高速公路综合管理系统

当前交通信息化建设中,电信光纤同步数字传输(SDH)的设备繁多、可靠性弱,面临交互困难、监控盲区,抗灾害天气差等问题.本文构建了以W(宽带)、N(网管)、R(识别)、T(转码)、M(应急接入)等为前缀的分布式监控数字单元(DMDU),其以W-DMDU为宽带接入平台,以R、T、M三大模块,实现了由单一交通通信向综合交通通信转变.T-DMDU针对异构视频难以交互,采用基于数字域的视频快速转码算法,构建路段异构视频转码服务器;R-DMDU以兴趣域(ROI)、摄像机自标定为基础,代替能见度仪进行视频能见度检测;针对监控盲点,M-DMDU建立基于无线宽带技术(WiFi)的监控系统,保证盲点视频实时回传.N-DMDU采用基于浏览器/客户端/服务器/代理(B/C/S/A)体系,构建省域路网联网管理系统,支持跨平台特性,满足用户不同需求.目前系统已用于江苏近1/5的高速公路,采用DMDU-2平台,完成16～32路视频回显、转码、检测,工作稳定,为综合交通提供较好的底层基础和试验点.

数字视频转码技术综述

由于终端设备、通信网络的异构性,视频转码成为近期研究的热点问题。从视频转码的体系结构、关键技术等方面来综述近期视频转码的研究进展以及有待解决的问题。

基于压缩域的多路视频流混合方法

针对视频会议应用中传统的像素域多路视频混合方法存在运算复杂度高、画面质量损伤的问题，提出了一种基于压缩域的替代方法，并给出了详细的码流映射算法步骤。该方法按照混合后多画面的空间位置关系，通过对输入的多路码流中宏块编码次序的重排和语法元素的映射，在码流级别将多路视频合成到同一画面中，并采取提前量化策略消除可能出现的二次量化失真，从而可兼具处理速度快和高保真的双重优点。以H．263为例验证了此方法的有效性。实验结果表明，与编解码器级联的方法相比，此方法的峰值信噪比（PSNR）平均提高2dB，运算效率提高百倍以上。此研究工作有望为正在制定的国际视频编码标准H．265贡献一种视频混合解决方案。

针对网络视频应用的视频转码技术综述

对视频转码的技术基础,尤其是3种典型的体系结构进行了简述和比较,然后讨论了利用从输入视频流中易于得到的信息,减少计算复杂度和改善视觉质量的方法。同时,对近年来出现的一些热点技术,如信息(包括水印和具有差错抑制特征的数据)嵌入的视频转码、H.264码流转码等进行了重点讨论。对视频转码技术的未来发展也进行了展望。

面向视频流的MEC缓存转码联合优化研究

为应对未来移动网络所面临的巨大挑战,业界提出了自适应比特流(adaptive bit rate,ABR)技术和移动边缘计算(mobile edge computing,MEC),旨在为用户提供高体验质量、低时延、高带宽和多样化的服务。联合ABR和MEC来优化视频内容分发,对于提高网络性能和用户体验质量具有重要意义。其中,各项网络资源的联合优化是重要的研究课题。首先对MEC进行了概述,然后基于面向自适应流的MEC缓存转码联合优化问题,对业界已有工作进行了分析和对比,并对未来面临的挑战和研究难点进行了归纳和展望。

面向移动网络的实时视频转码系统

针对宽带IPTV视频源向移动网络和移动终端提供视频服务需要解决的视频转码问题,设计开发以软件方式实现的实时视频转码系统,可将MPEG-4格式的高码率、高分辨率节目源实时转换为适合移动网络和移动终端的低码率、低分辨率视频。在误差补偿转码框架的基础上,提出一种自适应的转码时延保证方法,解决了转码计算复杂性和转码质量均衡的关键技术问题,保证了转码系统的实时性。测试结果表明,实现的转码系统视频转换质量损失少、实时性高。

压缩域MPEG-2到MPEG-4视频转码中不匹配宏块的复原算法

作为一种最有应用前景的先进的压缩编码标准，MPEG-4将会很快在视频传输、存储和剪辑等许多领域中得到应用。不过，MPEG-2和MPEG-4这两种优秀的视频系统肯定会在很长一段时间内共存。本文提出了一种基于MC-DCT域的转码方法。重点解决了两种标准视频流中宏块编码模式不匹配的一些关键问题。仿真结果表明，本算法不仅能有效地实现MPEG-2到MPEG-4的转码，满足实时性和低处迟的需求，而且能隐藏或复原编码模式不匹配宏块，明显地改变视觉质量。

分布式系统Hadoop平台的视频转码

在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(Hadoop Distributed File System)进行视频存储,利用MapReduce思想和FFMPEG进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce编程框架的分布式转码方案,即Mapper端进行转码和Reducer端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结果表明,相比单机转码,提出的系统在采用8台机器并行转码时,可以节约80%左右的时间。

一种基于分层率失真优化的容错性视频转码算法

容错性视频转码能够在已压缩视频流中嵌入容错性工具,以增强视频流面向无线信道的抗误码能力.但是容错性工具的嵌入在降低失真的同时也会增加码率,因此需要进行率失真优化.针对这一问题,文中提出了一种基于分层率失真优化的容错性转码算法.该算法通过在帧层和宏块层分别对重同步标记的插入和帧内宏块的刷新进行不同粒度的优化来提高算法的灵活性,通过在帧层考虑帧内宏块刷新、帧内预测和运动矢量预测的影响来提高算法的精确性,通过在宏块层减少候选模式的数量来降低算法的复杂度.实验表明,该算法与基于单层率失真优化的算法相比,信噪比可获得0.6～1.1dB的增益,复杂度最多可降低25%.