码率自适应算法是HTTP自适应流技术的热点和难点。提出一种综合网络带宽和缓存两个因素的终端码率自适应算法(Combined with Bandwidth and Buffer,CBB)。该算法采用"探测"的机制在应用层上估算网络实时带宽,避免视频码率频...码率自适应算法是HTTP自适应流技术的热点和难点。提出一种综合网络带宽和缓存两个因素的终端码率自适应算法(Combined with Bandwidth and Buffer,CBB)。该算法采用"探测"的机制在应用层上估算网络实时带宽,避免视频码率频繁切换;然后构建随缓存状态动态变化的平滑因子模型,并基于指数加权移动平均(EWMA)实现带宽的平滑处理;利用推动缓存趋近均衡级别变化的调度策略,尽可能使缓存区的数据量处于均衡的范围。整个算法经带宽估算、平滑处理、量化及确定调度时间构成一个循环作用的闭环。在使用MPEG-DASH标准的参考平台libdash上验证该算法的性能,结果表明,在变化的网络状况中所提算法表现良好。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
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 net...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.展开更多
文摘码率自适应算法是HTTP自适应流技术的热点和难点。提出一种综合网络带宽和缓存两个因素的终端码率自适应算法(Combined with Bandwidth and Buffer,CBB)。该算法采用"探测"的机制在应用层上估算网络实时带宽,避免视频码率频繁切换;然后构建随缓存状态动态变化的平滑因子模型,并基于指数加权移动平均(EWMA)实现带宽的平滑处理;利用推动缓存趋近均衡级别变化的调度策略,尽可能使缓存区的数据量处于均衡的范围。整个算法经带宽估算、平滑处理、量化及确定调度时间构成一个循环作用的闭环。在使用MPEG-DASH标准的参考平台libdash上验证该算法的性能,结果表明,在变化的网络状况中所提算法表现良好。
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。
基金This work has been supported in part by the Austrian Research Promotion Agency(FFG)under the APOLLO and Karnten Fog project.
文摘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.
