Proactive caching is considered a cost-effective method to address the backhaul bottleneck problem in cellular network. In this paper, we propose a novel popularity predicting-caching procedure that takes raw video data as an input to determine an optimal cache placement policy, which deals with both published and unpublished videos. To anticipate the popularity of unpublished videos of which the statistical information is not available, we apply the content-based approach by extracting and condensing video features into a high-dimensional vector. Subsequently, we form G clusters of features representing the potential video categories (VCs) and map the feature vector into a G-dimensional space, where each element indicates the percentage to which the video contains the features of the corresponding VC. Finally, we train a prediction model to foresee the popularity, where the set of published videos is used as training data. Last, the prediction with expert advice method is used to update the training set, and to gain insight into how the predictor output will deviate from the best expert prediction, we address the concept of expected cumulative loss and derive the analytical expression for its upper bound. Extensive simulation results are shown to gain insight into our proposed system subject to different factors, such as network size, cache capacity, and user's preference profile. In summary, we show that applying intelligence-based content-aware proactive caching is an efficient approach to significantly improving the operation of cellular networks in the future.
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