基于用户行为分析的视频点播系统优化技术研究
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摘要
随着网络硬件与软件技术的飞速发展,网络传输的主要内容逐渐由早期的文本转变为多媒体数据。视频点播服务作为当今网络带宽的主要占用者已经成为最受欢迎的应用。大量的理论研究工作和商用系统在近些年涌现出来。在视频点播系统中支持VCR操作特别是任意跳转,同时保证一定的播放流畅度,在当前网络带宽限制条件下仍是一个挑战性的工作。如何减小用户等待时间,保证播放流畅度以提高用户观看体验是一个亟待解决的问题。
不仅仅是视频点播系统的被动使用者,用户能以参与者的身份与系统进行交互,影响系统的表现。本文通过分析用户的观看行为来进行视频点播系统的优化,以达到提高用户观看体验的目的。具体的,本文主要研究贡献如下：
1.提出一种视频内用户兴趣的挖掘方法。通过对一个真实视频点播系统日志记录的分析,发现用户的跳转行为具有二义性,不能统一对待。因此使用最大期望算法将用户跳转行为分为两类：满意跳转与非满意跳转。使用该概率划分对视频每个位置进行打分,可以获得用户视频块兴趣得分。它以数值方式表现了用户对视频内不同位置的兴趣水平。通过应用于不同类型的视频,验证了所提出的算法能准确的描述用户兴趣。
2.给出一种视频热点位置自动标记算法。基于用户兴趣,该算法能自动地标记不同类型视频的热点位置,可被用来引导用户跳转,提高用户观看体验。通过应用于大量视频,所提算法的标记结果能在多方面优于传统的基于图像处理的视频语义分析方法,包括准确度、普适性、稳定性、运行效率等。同时给出了一个视频热点位置标记的应用实例：BitTorrent下载中视频热点位置快速预览策略。仿真和Planetlab实验都表明了预览策略的可行性。
3.提出用户播放的动态启动阈值策略。由于用户观看时间分布极为不均,因此现有的固定启动阈值方法并不能很好的适应用户观看行为。基于此观察,提出了动态启动阈值算法。在高斯流量的基础上,它能结合用户的观看兴趣和当前下载带宽动态的计算最优启动阂值。此阈值能在保证用户指定播放流畅度的前提下最小化等待时间。如果使用此阈值实时控制用户的缓存长度还可以减少用户流量消耗,这在移动网络中是十分重要的。使用真实用户观看日志数据的仿真实验证明了动态启动阈值的有效性。
With the fast development of network technology including both hardware and software, the main content delivered on Internet has been changed from text to mul-timedia. The video-on-demand (VoD) service who contributes the majority of traffic has become the most popular application on Internet. In the past few years, lots of re-search works and commercial systems emerge in large numbers. It is challenging to support the VCR functionality, especially the freewill jumps, while maintaining a s-mooth streaming quality in VoD systems within the current network conditions. How to minimize users' wait time while guarantee a certain playback fluency for improving user experience urgently need to be addressed.
A customer in a VoD system is not just a passive user but a participator, he can produce VCR operations to affect the system performance. In this paper, we analyze users' viewing behaviors to optimize the VoD system designs for improving users'ex-periences. Specifically, the contributes of this paper are summarized as follows:
1. A method of mining users' interests inside a video is proposed. Through analyz-ing the statistics of log files of a real-world VoD system, we find that users may produce seeking operators with two different underlying meanings and they can't be treated in the same way. So we classify users' jumping behaviors into two kinds:satisfactory and unsatisfactory jumps. Based on the two classification probability, we score every video segment which can represent the levels of users' interests in different positions of the video. By applying to different kinds of videos, the results show our scoring algorithm can capture the users' interests accurately.
2. We present a bookmarking algorithm for automated marking the highlights of a video. Based on users' interests, the algorithm can automatically bookmarking different kinds of videos' highlights which can be utilized to guide users' seeking and improve experiences. Applying the algorithm on a large number of videos, the mark results prove this algorithm can get better performance than the traditional video semantic analysis methods which are based on image processing, including accuracy, universality, stabili-ty, efficiency and etc. We also give an application example of highlight bookmarks:fast preview of video hotspots in BitTorrent downloading. The simulation and PlanetLab experiments prove the feasibility of our strategy.
3. A dynamic start-up threshold algorithm of playback is addressed. As users' viewing time distributes extremely uneven, the existing fixed start-up threshold method can't adapt to users' viewing behaviors. Based on this observation, we propose a dy-namic start-up threshold algorithm. On the basis of the Gaussian traffic, our algorithm combines users' viewing interests with current download bandwidth for dynamically calculating the optimal threshold which can minimize users' wait time while ensure a specified playback fluency. If we use this threshold to control users'buffer length, the traffic consumptions of clients can be reduced which is very important in mobile networks. The simulation experiment using real log files proves the effectiveness of dynamic start-up threshold.
不仅仅是视频点播系统的被动使用者,用户能以参与者的身份与系统进行交互,影响系统的表现。本文通过分析用户的观看行为来进行视频点播系统的优化,以达到提高用户观看体验的目的。具体的,本文主要研究贡献如下：
1.提出一种视频内用户兴趣的挖掘方法。通过对一个真实视频点播系统日志记录的分析,发现用户的跳转行为具有二义性,不能统一对待。因此使用最大期望算法将用户跳转行为分为两类：满意跳转与非满意跳转。使用该概率划分对视频每个位置进行打分,可以获得用户视频块兴趣得分。它以数值方式表现了用户对视频内不同位置的兴趣水平。通过应用于不同类型的视频,验证了所提出的算法能准确的描述用户兴趣。
2.给出一种视频热点位置自动标记算法。基于用户兴趣,该算法能自动地标记不同类型视频的热点位置,可被用来引导用户跳转,提高用户观看体验。通过应用于大量视频,所提算法的标记结果能在多方面优于传统的基于图像处理的视频语义分析方法,包括准确度、普适性、稳定性、运行效率等。同时给出了一个视频热点位置标记的应用实例：BitTorrent下载中视频热点位置快速预览策略。仿真和Planetlab实验都表明了预览策略的可行性。
3.提出用户播放的动态启动阈值策略。由于用户观看时间分布极为不均,因此现有的固定启动阈值方法并不能很好的适应用户观看行为。基于此观察,提出了动态启动阈值算法。在高斯流量的基础上,它能结合用户的观看兴趣和当前下载带宽动态的计算最优启动阂值。此阈值能在保证用户指定播放流畅度的前提下最小化等待时间。如果使用此阈值实时控制用户的缓存长度还可以减少用户流量消耗,这在移动网络中是十分重要的。使用真实用户观看日志数据的仿真实验证明了动态启动阈值的有效性。
With the fast development of network technology including both hardware and software, the main content delivered on Internet has been changed from text to mul-timedia. The video-on-demand (VoD) service who contributes the majority of traffic has become the most popular application on Internet. In the past few years, lots of re-search works and commercial systems emerge in large numbers. It is challenging to support the VCR functionality, especially the freewill jumps, while maintaining a s-mooth streaming quality in VoD systems within the current network conditions. How to minimize users' wait time while guarantee a certain playback fluency for improving user experience urgently need to be addressed.
A customer in a VoD system is not just a passive user but a participator, he can produce VCR operations to affect the system performance. In this paper, we analyze users' viewing behaviors to optimize the VoD system designs for improving users'ex-periences. Specifically, the contributes of this paper are summarized as follows:
1. A method of mining users' interests inside a video is proposed. Through analyz-ing the statistics of log files of a real-world VoD system, we find that users may produce seeking operators with two different underlying meanings and they can't be treated in the same way. So we classify users' jumping behaviors into two kinds:satisfactory and unsatisfactory jumps. Based on the two classification probability, we score every video segment which can represent the levels of users' interests in different positions of the video. By applying to different kinds of videos, the results show our scoring algorithm can capture the users' interests accurately.
2. We present a bookmarking algorithm for automated marking the highlights of a video. Based on users' interests, the algorithm can automatically bookmarking different kinds of videos' highlights which can be utilized to guide users' seeking and improve experiences. Applying the algorithm on a large number of videos, the mark results prove this algorithm can get better performance than the traditional video semantic analysis methods which are based on image processing, including accuracy, universality, stabili-ty, efficiency and etc. We also give an application example of highlight bookmarks:fast preview of video hotspots in BitTorrent downloading. The simulation and PlanetLab experiments prove the feasibility of our strategy.
3. A dynamic start-up threshold algorithm of playback is addressed. As users' viewing time distributes extremely uneven, the existing fixed start-up threshold method can't adapt to users' viewing behaviors. Based on this observation, we propose a dy-namic start-up threshold algorithm. On the basis of the Gaussian traffic, our algorithm combines users' viewing interests with current download bandwidth for dynamically calculating the optimal threshold which can minimize users' wait time while ensure a specified playback fluency. If we use this threshold to control users'buffer length, the traffic consumptions of clients can be reduced which is very important in mobile networks. The simulation experiment using real log files proves the effectiveness of dynamic start-up threshold.
引文
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