Abstract
Smart device users spend most of the fragmentation time in the entertainment applications such as videos and films. The migration and reconstruction of video copies can improve the storage efficiency in distributed mobile edge computing, and the prediction of video hits is the premise for migrating video copies. This paper proposes a new prediction approach for video hits based on the combination of correlation analysis and wavelet neural network (WNN). This is achieved by establishing a video index quantification system and analyzing the correlation between the video to be predicted and already online videos. Then, the similar videos are selected as the influencing factors of video hits. Compared with the autoregressive integrated moving average (ARIMA) and gray prediction, the proposed approach has a higher prediction accuracy and a broader application scope.
Highlights
Smart device users spend more than 70% of the fragmentation time in the entertainment applications such as videos and films. e video content providers (e.g., Netflix) desire to know the future video view counts of all their videos, especially the new ones, to provide a better experience for consumers
E current research [13] shows that the migration and reconstruction of video copies is an effective means to improve the storage efficiency, and the prediction of the number of video hits is the prerequisite for migrating video copies. ere are many data prediction and recommendation approaches [14, 15]; they do not consider the fine-grained granularity of every video copy and cannot provide the required information for migration and reconstruction of video copies
Based on the Security and Communication Networks combination of correlation analysis and wavelet neural network (WNN), this paper proposes a new prediction approach for video hits by analyzing the correlation between the video to be predicted and already online videos, and selecting the similar videos as the influencing factors
Summary
Smart device users spend more than 70% of the fragmentation time in the entertainment applications such as videos and films. e video content providers (e.g., Netflix) desire to know the future video view counts of all their videos, especially the new ones, to provide a better experience for consumers. E distributed model brings many problems for video service providers, such as how to keep the storage efficiency and improve the energy efficiency of storage [4, 5]. E current popular cloud storage platforms generally use a static storage mechanism, that is, setting the number of copies before placing them, such as Google File System [9], Hadoop Distributed File System (HDFS) [10], and Amazon. E current research [13] shows that the migration and reconstruction of video copies is an effective means to improve the storage efficiency, and the prediction of the number of video hits is the prerequisite for migrating video copies. Based on the Security and Communication Networks combination of correlation analysis and wavelet neural network (WNN), this paper proposes a new prediction approach for video hits by analyzing the correlation between the video to be predicted and already online videos, and selecting the similar videos as the influencing factors
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