With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G netw...With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.展开更多
Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for ...Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.展开更多
Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media p...Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lacking a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by proposing such a framework, describing its architecture, providing an example evaluation, and discussing open issues.展开更多
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 A...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.展开更多
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 achi...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.展开更多
对于日益发展的移动互联网来说,流媒体是其最重要最有需求和市场的应用之一。本论文以Http Live Streaming技术为背景,详细介绍了Android平台架构和Android NDK开发,并在此基础上介绍并设计了移动流媒体直播系统,实现了无线网络视频的...对于日益发展的移动互联网来说,流媒体是其最重要最有需求和市场的应用之一。本论文以Http Live Streaming技术为背景,详细介绍了Android平台架构和Android NDK开发,并在此基础上介绍并设计了移动流媒体直播系统,实现了无线网络视频的传输。最后,通过性能测试,实现了客户端采集编码功能。展开更多
Application Layer Multicast (ALM) can greatly reduce the load of a server by leveraging the outgoing bandwidth of the participating nodes. However, most proposed ALM schemes become quite complicated and lose bandwidth...Application Layer Multicast (ALM) can greatly reduce the load of a server by leveraging the outgoing bandwidth of the participating nodes. However, most proposed ALM schemes become quite complicated and lose bandwidth efficiency if they try to deal with networks that are significantly heterogeneous or time-varying. In earlier work, we proposed MutualCast, an ALM scheme with fully connected mesh that quickly adapts to the time-varying networks, while achieving provably optimal throughput performance. In this paper, we study how MutualCast can be paired with adaptive rate control for streaming media. Specifically, we combine Optimal Rate Control (ORC), our earlier control-theoretical framework for quality adaptation, with the MutualCast delivery scheme. Using multiple bit rate video content, we show that the proposed system can gracefully adjust the common quality received at all the nodes while maintaining a continuous streaming experience at each, even when the network undergoes severe, uncorrelated bandwidth fluctuations at different peer nodes.展开更多
In this paper, we propose a practical design and implementation of network-adaptive high definition (HD) MPEG-2 video streaming combined with cross-layered channel monitoring (CLM) over the IEEE 802.11a wireless local...In this paper, we propose a practical design and implementation of network-adaptive high definition (HD) MPEG-2 video streaming combined with cross-layered channel monitoring (CLM) over the IEEE 802.11a wireless local area network (WLAN). For wireless channel monitoring, we adopt a cross-layered approach, where an access point (AP) periodically measures lower layers such as medium access control (MAC) and physical (PHY) transmission information (e.g., MAC layer loss rate) and then sends the monitored information to the streaming server application. The adaptive streaming server with the CLM scheme reacts more quickly and efficiently to the fluctuating wireless channel than the end-to-end application-layer monitoring (E2EM) scheme. The streaming server dynamically performs priority-based frame dropping to adjust the sending rate according to the measured wireless channel condition. For this purpose, the proposed streaming system nicely provides frame-based prioritized packetization by using a real-time stream parsing module. Various evaluation results over an IEEE 802.11a WLAN testbed are provided to verify the intended Quality of Service (QoS) adaptation capability. Experimental results showed that the proposed system can mitigate the quality degradation of video streaming due to the fluctuations of time-varying channel.展开更多
近年来,基于HTTP(Hyper Text Transport Protocol)的网络视频流传输方式越来越受到人们的关注,同时出现了若干相近的解决方案,实现了在HTTP上的动态自适应视频流传输。MPEG和3GPP在这些方案的基础上制定了一个新的基于HTTP的网络动态自...近年来,基于HTTP(Hyper Text Transport Protocol)的网络视频流传输方式越来越受到人们的关注,同时出现了若干相近的解决方案,实现了在HTTP上的动态自适应视频流传输。MPEG和3GPP在这些方案的基础上制定了一个新的基于HTTP的网络动态自适应流传输标准——DASH,并成为ISO/IEC国际标准于2012年正式发布。DASH系统工作于普通的Web服务器/客户端方式,它将同一内容的多个不同质量的视频流分片、定位和描述,使得这些视频分片能够如同普通文件一样通过HTTP协议在网络中传输。用户可以向服务器请求所需的视频,动态自适应地根据自己的网络带宽、接受能力进行选择、接收、解码和播放。DASH为视频流传输提供了一种高效、便捷的传送方式,特别适用于视频直播、点播、多屏显示等业务。随着DASH标准的逐渐完善,基于HTTP的网络视频流传输必将具有更加广泛的应用前景。展开更多
分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streamin...分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。展开更多
数据流是一类具有高生成率、动态分布特性的数据,其异常检测旨在从这一类数据中发现偏离预期行为的数据流,从而为医疗、工业生产、金融等诸多领域的决策提供支持。现有数据流异常检测方法普遍面临参数敏感性高、时空开销大、阈值选取难...数据流是一类具有高生成率、动态分布特性的数据,其异常检测旨在从这一类数据中发现偏离预期行为的数据流,从而为医疗、工业生产、金融等诸多领域的决策提供支持。现有数据流异常检测方法普遍面临参数敏感性高、时空开销大、阈值选取难等问题。为了解决上述问题,提出一种基于变密度的自适应数据流的异常检测方法。首先定义了可变局部离群因子(Va-riable Local Outlier Factor,VLOF),VLOF通过对比数据点在并行的不同k值的邻域窗口下,其局部可达密度和局部异常因子的变化情况,度量数据点的密度分布,降低单一k近邻密度度量导致的结果不准确。其次,计算VLOF与k值的相对增长率和绝对均值率,以反映数据流的动态变化趋势,并将适应这种动态变化趋势的数据点定义为核心点,通过核心点加快对后续正常点的判断。最后,将相对增长率和绝对均值率作为数据点理论分布的度量指标,计算理论分布和新数据点实际分布的差异,从而自适应地将偏离理论分布的点识别为异常。为了验证提出算法的有效性,在多个UCI数据集和真实数据集下与8个算法进行对比实验,实验结果表明:与基线模型相比,所提方法在精确率、召回率、F1性能指标上表现良好,且时间和空间效率也有相应提升。展开更多
基金support by the Major National Science and Technology Projects (No. 2018ZX03001014-003)
文摘With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.
基金fully supported under the National Natural Science Funds(Project Number:61501042 and 61302089)National High Technology Research and Development Program(863)of China(Project Number:2015AA016101 and 2015AA015702)BUPT Special Program for Youth Scientific Research Innovation(Grant No.2015RC10)
文摘Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.
基金supported in part by the Austrian Research Promotion Agency(FFG)under the next generation video streaming project "PROMETHEUS"
文摘Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lacking a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by proposing such a framework, describing its architecture, providing an example evaluation, and discussing open issues.
基金supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200486.
文摘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.
基金This work was supported by National Natural Science Foundation of China(No.61771070)National Natural Science Foundation of China(No.61671088).
文摘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.
文摘对于日益发展的移动互联网来说,流媒体是其最重要最有需求和市场的应用之一。本论文以Http Live Streaming技术为背景,详细介绍了Android平台架构和Android NDK开发,并在此基础上介绍并设计了移动流媒体直播系统,实现了无线网络视频的传输。最后,通过性能测试,实现了客户端采集编码功能。
文摘Application Layer Multicast (ALM) can greatly reduce the load of a server by leveraging the outgoing bandwidth of the participating nodes. However, most proposed ALM schemes become quite complicated and lose bandwidth efficiency if they try to deal with networks that are significantly heterogeneous or time-varying. In earlier work, we proposed MutualCast, an ALM scheme with fully connected mesh that quickly adapts to the time-varying networks, while achieving provably optimal throughput performance. In this paper, we study how MutualCast can be paired with adaptive rate control for streaming media. Specifically, we combine Optimal Rate Control (ORC), our earlier control-theoretical framework for quality adaptation, with the MutualCast delivery scheme. Using multiple bit rate video content, we show that the proposed system can gracefully adjust the common quality received at all the nodes while maintaining a continuous streaming experience at each, even when the network undergoes severe, uncorrelated bandwidth fluctuations at different peer nodes.
基金Project (No. R05-2004-000-10987-0) partly supported by the Basic Research Program of the Korea Research Foundation
文摘In this paper, we propose a practical design and implementation of network-adaptive high definition (HD) MPEG-2 video streaming combined with cross-layered channel monitoring (CLM) over the IEEE 802.11a wireless local area network (WLAN). For wireless channel monitoring, we adopt a cross-layered approach, where an access point (AP) periodically measures lower layers such as medium access control (MAC) and physical (PHY) transmission information (e.g., MAC layer loss rate) and then sends the monitored information to the streaming server application. The adaptive streaming server with the CLM scheme reacts more quickly and efficiently to the fluctuating wireless channel than the end-to-end application-layer monitoring (E2EM) scheme. The streaming server dynamically performs priority-based frame dropping to adjust the sending rate according to the measured wireless channel condition. For this purpose, the proposed streaming system nicely provides frame-based prioritized packetization by using a real-time stream parsing module. Various evaluation results over an IEEE 802.11a WLAN testbed are provided to verify the intended Quality of Service (QoS) adaptation capability. Experimental results showed that the proposed system can mitigate the quality degradation of video streaming due to the fluctuations of time-varying channel.
文摘近年来,基于HTTP(Hyper Text Transport Protocol)的网络视频流传输方式越来越受到人们的关注,同时出现了若干相近的解决方案,实现了在HTTP上的动态自适应视频流传输。MPEG和3GPP在这些方案的基础上制定了一个新的基于HTTP的网络动态自适应流传输标准——DASH,并成为ISO/IEC国际标准于2012年正式发布。DASH系统工作于普通的Web服务器/客户端方式,它将同一内容的多个不同质量的视频流分片、定位和描述,使得这些视频分片能够如同普通文件一样通过HTTP协议在网络中传输。用户可以向服务器请求所需的视频,动态自适应地根据自己的网络带宽、接受能力进行选择、接收、解码和播放。DASH为视频流传输提供了一种高效、便捷的传送方式,特别适用于视频直播、点播、多屏显示等业务。随着DASH标准的逐渐完善,基于HTTP的网络视频流传输必将具有更加广泛的应用前景。
文摘分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。
文摘数据流是一类具有高生成率、动态分布特性的数据,其异常检测旨在从这一类数据中发现偏离预期行为的数据流,从而为医疗、工业生产、金融等诸多领域的决策提供支持。现有数据流异常检测方法普遍面临参数敏感性高、时空开销大、阈值选取难等问题。为了解决上述问题,提出一种基于变密度的自适应数据流的异常检测方法。首先定义了可变局部离群因子(Va-riable Local Outlier Factor,VLOF),VLOF通过对比数据点在并行的不同k值的邻域窗口下,其局部可达密度和局部异常因子的变化情况,度量数据点的密度分布,降低单一k近邻密度度量导致的结果不准确。其次,计算VLOF与k值的相对增长率和绝对均值率,以反映数据流的动态变化趋势,并将适应这种动态变化趋势的数据点定义为核心点,通过核心点加快对后续正常点的判断。最后,将相对增长率和绝对均值率作为数据点理论分布的度量指标,计算理论分布和新数据点实际分布的差异,从而自适应地将偏离理论分布的点识别为异常。为了验证提出算法的有效性,在多个UCI数据集和真实数据集下与8个算法进行对比实验,实验结果表明:与基线模型相比,所提方法在精确率、召回率、F1性能指标上表现良好,且时间和空间效率也有相应提升。
