Knowledge-based configuration of videos using feature models

User-centricity and variability play an increasingly important role in various application domains. A trend in the context of creating and sharing videos is to personalize contents and related navigation features. Such a personalization is needed to be able to take into account the preferences of users in terms of preferred contents, existing knowledge levels (e.g., in the context of teaching videos), and available time for watching a video. In this paper, we present an approach to define variability properties of videos. In this context, we show how the different modeling concepts of feature models can be used to represent variability properties of videos and also discuss related open research challenges.