Improving the QoS of MPEG2 streaming over wireless LANs

We propose a simulation-based approach to quality-of-service (QoS) improvement of multimedia streaming over wireless LANs. Since multimedia applications have stringent real time constraints, transmission schemes which rely on massive use of redundancy are not viable options. We present a portable simulation framework consisting of re-configurable software components. We build a simulation model which reflects the peculiarities of the MPEG-2 coding technique and streaming process by specializing the objects of the framework. We propose a set of strategies, which improve the QoS of the transmission, while limiting the amount of redundancy used. Finally, we conduct a quality and cost analysis of the proposed alternatives via detailed simulation of a case study system.     

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.     

Enhancing wireless video streaming using lightweight approximate authentication

In this paper, we propose a novel lightweight approximate authentication algorithm that provides efficient protection for wireless video streaming where attacks on the stream are possible, but classical integrity protection algorithms are impractical because bit errors occur naturally. The benefits of the proposed algorithm over other algorithms are fast execution and small message authentication code size. Moreover, the approximate authentication supports error resilient video decoding by dropping seriously damaged packets, thus improving the perceived quality of the video stream. The performance of the algorithm is demonstrated via numerical analysis, simulations and measurements over modeled and real wireless channels.     

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.

视频点播系统研究与实现

目前,视频点播技术方兴未艾,在教育领域上渐渐受到了推广应用。本文主要阐述了视频点播系统的功能与应用,分析了视频点播的工作原理,解析了B/S开发模式、流媒体技术和MPEG编码技术,目的在于推动视频点播技术在多媒体教学中的推广与普及。     

可视电话系统的QoS管理

ＱｏＳ管理近年来引起了人们的关注,尤其是分布式多媒体系统中ＱｏＳ管理更是一研究热点,本文则独辟蹊径,探讨点到点可视电话系统中的端到端的ＱｏＳ管理,以填补当前研究中的一个空白,本文给出了可视电话系统中的ＱｏＳ参考模型,管理策略以及可视电话系统的结构。     

基于资源约束的流媒体服务QoS策略

对于流媒体服务，端表现质量是衡量系统性能的关键指标。本文对流媒体服务瓶颈资源进行了分析并提出了基于资源约束的策略来保证端服务质量。通过仿真测试，验证了QoS策略的有效性。     

基于MPEG双向视频流实现VCR功能的方法研究

如何有效地实现基于MPEG视频流的数字VCR(Digilal Video Cassette Recording)功能是视频流服务的一个技术挑战,提出在服务器端建立双向视频流,采用具有最小花费的帧选择方案,在需要网络带宽和解码复杂度最小的情况下有效地实现数字VCR操作。由反向视频流组成的双向流可以有效地执行倒播功能,但在执行其他VCR功能时带来视频帧的动态抖动问题及所需网络带宽和解码复杂度继续减少的问题;直接参考流组成的双向流可以很好地解决这些问题并能更有效地实现VCR的操作。     

Sudden scene change detection in compressed video using interpolated macroblocks in B-frames

This paper addresses an important area in video processing, namely compressed domain processing. For video indexing, video scene transition detection is an essential step to segment the video. Current techniques for scene change detection tend to suffer from a major limitation as most of them cannot identify scene transitions in the compressed domain. Since most video is expected to be stored in the compressed domain, scene transition detection in this domain is highly desirable. In this paper an algorithm for video scene change detection is proposed to overcome this limitation. In this scheme, properties of the B-frames are used as it is capable of measuring the correlation between two adjacent reference frames. The results show that this scheme performs better than schemes based on P-frames. Proposed scheme can be directly applied with compressed data with minimum decompression and hence it is computationally efficient and makes real time implementations possible. Results show that video scene transitions can be identified satisfactorily with the proposed scheme.     

流媒体应用中的QoS问题分析

全面分析了流媒体系统中QoS问题的制约因素，从QoS实现的两个基本条件出发，得到了QoS问题的全面解决方法。该方法从内容、网络、传输策略和服务器四个方面综合考虑，对QoS问题的解决以及流媒体应用系统的建设具有指导意义。     

XOR-based frame loss recovery scheme for video streaming

High coding dependencies among video frames suffer from vulnerability to packet loss, which impacts the playback quality of video streaming. In this paper, according to the characteristics of MPEG4/H.264 encoding methods, we propose a simple and low-complexity XOR-based FEC frame loss recovery scheme. Within an entire Group of Pictures (GOP), the proposed scheme shows the ability to recover simultaneously I-frame loss and one P-frame loss. The high frame loss resilience improves the playback QoS of compressed video streaming. The mathematical analysis reveals that the proposed scheme has 72.7% performance improvement than no frame loss protection in term of full GOP frames successful decoding rate.     

Optimized scalable cache management for video streaming system

An important research issue in video streaming is how to efficiently utilize the network resources to provide clients instant access to multiple video objects. Caching strategy and transmission scheme are the two essential points inside the video streaming framework. Recent research efforts on them are not sufficient due to their inflexible support for scalable encoded video streams and heterogeneous requests from clients. In this paper, we propose an optimized caching strategy (OCS) and a scalable transmission scheme (STS) for scalable coded video streaming. By exploring the characteristics of video streaming workload and system design objectives, OCS and STS work efficiently to minimize both network bandwidth cost and user access latency. Firstly, we analyze the caching problem for the proxy-assisted video streaming system and derive a maneuverable caching scenario. Secondly, we develop an efficient transmission scheme for scalable coded videos. Thirdly, we formulate a multi-objective optimization model with closed-form expressions to obtain the optimized caching strategy. Finally, with designed algorithms, an excellent compromise between two competing objectives (minimizing the bandwidth cost and the access latency) is achieved. We start our evaluation by studying the optimized caching strategy for a single video object. Then we apply the strategy to multiple video objects and illustrate the tradeoff between the optimization objectives. Our evaluation results show that compared with other caching strategies, the proposed optimized scalable caching strategy can achieve a significant reduction in bandwidth cost with even a small proxy cache size. Meanwhile, the best performance (in terms of bandwidth cost) is obtained together with the proposed scalable batch-patching transmission scheme.

OCS: An effective caching scheme for video streaming on overlay networks

Video streaming is vital for many important applications such as distance learning, digital video libraries, and movie-on-demand. Since video streaming requires significant server and networking resources, caching has been used to reduce the demand on these resources. In this paper, we propose a novel collaboration scheme for video caching on overlay networks, called Overlay Caching Scheme (OCS), to further minimize service delays and loads placed on an overlay network for video streaming applications. OCS is not a centralized nor a hierarchical collaborative scheme. Despite its design simplicity, OCS effectively uses an aggregate storage space and capability of distributed overlay nodes to cache popular videos and serve nearby clients. Moreover, OCS is light-weight and adaptive to clients’ locations and request patterns. We also investigate other video caching techniques for overlay networks including both collaborative and non-collaborative ones. Compared with these techniques on topologies inspired from actual networks, OCS offers extremely low average service delays and approximately half the server load. OCS also offers smaller network load in most cases in our study.

Enhancing throughput for streaming applications running on cluster systems

The exploitation of throughput in a parallel application that processes an input data stream is a difficult challenge. For typical coarse-grain applications, where the computation time of tasks is greater than their communication time, the maximum achievable throughput is determined by the maximum task computation time. Thus, the improvement in throughput above this maximum would eventually require the modification of the source code of the tasks. In this work, we address the improvement of throughput by proposing two task replication methodologies that have the target throughput to be achieved as an input parameter. They proceed by generating a new task graph structure that permits the target throughput to be achieved. The first replication mechanism, named DPRM (Data Parallel Replication Mechanism), exploits the inner task data parallelism. The second mechanism, named TCRM (Task Copy Replication Mechanism), creates new execution paths inside the application task graph structure that allows more than one instance of data to be processed concurrently. We evaluate the effectiveness of these mechanisms with three real applications executed in a cluster system: the MPEG2 video compressor, the IVUS (Intra-Vascular Ultra-Sound) medical image application and the BASIZ (Bright and SAtured Images Zone) video processing application. In all these cases, the obtained throughput was greater after applying the proposed replication mechanism than what the application could provide with the original implementation.     

Proxy-assisted periodic broadcast for video streaming with multiple servers

Large scale video streaming over the Internet requires a large amount of resources such as server I/O bandwidth and network bandwidth. A number of video delivery techniques can be used to lower these requirements. Periodic broadcast by a central server combined with proxy caching offers a significant reduction of the aggregate network and server I/O bandwidth usage. However, the resources available to a single server are still limited. In this paper we propose a system with multiple geographically distributed servers. The problem of multiple servers for periodic broadcast is quite different from the problem of object location for multiple web servers. Multiple servers offer increased amount of resources and service availability and may potentially allow a further reduction of network bandwidth usage. On the other hand, the benefit of periodic broadcast mostly comes from high demand videos. With multiple servers holding a video, the demand of the video at each server is reduced. Therefore, it is a challenge to use multiple servers efficiently. We first analyze the dependence of the resource requirements on the number and locations of the servers. Based on the character of the function describing such a dependence, we formulate and solve the problem of video location and delivery, in a way that minimizes resource usage. We explore a trade-off between network and I/O bandwidth requirements. We evaluate our proposed solutions through a number of tests.

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。     

Parallel video streaming optimizing network throughput

In the Internet, video streaming services, in which users can enjoy videos at home, are becoming popular. Video streaming with high definition TV (HDTV) or ultra high definition video (UHDV) quality will be also provided and widely demanded in the future. However, the transmission bit-rate of high-quality video streaming is quite large, so generated traffic flows will cause link congestion. In the Internet, routes that packets take are determined using static link weights, so the network productivity, i.e., the maximum achievable throughout by the network, is determined by the capacity of a bottleneck link with the maximum utilization, although utilizations of many links remain low level. Therefore, when providing streaming services of rich content, i.e., videos with HDTV or UHDV quality, it is important to flatten the link utilization, i.e., reduce the maximum link utilization. We propose that ISPs use multiple servers to deliver rich content to balance the link utilization and propose server allocation and server selection methods for parallel delivery. We evaluate the effect of parallel delivery using 23 actual commercial ISP networks.     

A live video streaming system for intuitive human-system interaction

In this paper, we propose an intuitive live video streaming system based on virtual reality technologies among people who are far apart. This system is a kind of server-client system, and can provide remote users with virtual 3D audiovisual fields in real time via a very-high-speed network. The server captures audio and video data from its clients, compiles them into a 3D audiovisual scene at a virtual conference, and broadcasts it to the clients. At the present stage, our system captures 2 videos and creates one 3D video at a time. Our system can play 3D audiovisual contents on Windows XP systems as well as on CAVE systems. Currently, our system can play the 3D video contents at about 2.36 fps under a LAN environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

一种基于QoS的远程监控系统模型

1.概述随着电子技术、信息处理技术和传感器技术的发展,计算机及外围设备性能越来越高但价格越来越低,数字视频监控系统逐渐从金融、高级宾馆等安全防范重要部门走向普通厂矿、企业甚至家庭,视频监控系统的用途也逐渐从安全防范演变成企业的普通管理工具。远程视频监控突破了传统监控     

Energy optimization for mobile video streaming via an aggregate model

Wireless video streaming on smartphones drains a significantly large fraction of battery energy, which is primarily consumed by wireless network interfaces for downloading unused data and repeatedly switching radio interface. In this paper, we propose an energy-efficient download scheduling algorithm for video streaming based on an aggregate model that utilizes user’s video viewing history to predict user behavior when watching a new video, thereby minimizing wasted energy when streaming over wireless network interfaces. The aggregate model is constructed by a personal retention model with users’ personal viewing history and the audience retention on crowd-sourced viewing history, which can accurately predict the user behavior of watching videos by balancing “user interest” and “video attractiveness”. We evaluate different users streaming multiple videos in various wireless environments and the results illustrate that the aggregate model can help reduce energy waste by 20 % on average. In addition, we also discuss implementation details and extensions, such as dynamically updating personal retention, balancing audience and personal retention, categorizing videos for accurate model.