In Content Delivery Networks (CDNs), knowing the popularity of video content helps the manager to take efficient decisions about which video content should be cached near the end users and also about the duplication degree of each video to satisfy the end user Quality of Experience. This paper focuses on predicting the popularity of video content, in terms of the number of requests. For that purpose, different software entities, called experts, compute the popularity value of each video content. Each expert uses its own prediction method. The accuracy of expert's prediction is evaluated by a loss function as the discrepancy between the prediction value and the real number of requests. We use real traces extracted from YouTube to compare different prediction methods and determine the best tuning of their parameters. The goal is to find the best trade-off between complexity and accuracy of the prediction methods used. Finally, we apply these prediction methods to caching. Prediction methods are compared in terms of cache Hit Ratio and Update Ratio with the well-known LFU caching strategy.