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.

中文翻译：

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移动边缘计算环境中视频点击量的预测方法

智能设备用户将大部分时间用于娱乐应用程序，例如视频和电影。视频副本的迁移和重构可以提高分布式移动边缘计算的存储效率，而视频点击量的预测是迁移视频副本的前提。本文提出了一种基于相关分析和小波神经网络（WNN）相结合的视频点击量预测新方法。这是通过建立视频索引量化系统并分析要预测的视频与已经在线的视频之间的相关性来实现的。然后，选择相似的视频作为视频点击量的影响因素。与自回归综合移动平均值（ARIMA）和灰色预测相比，