Over the recent years, user expectations of the ability of video search engines have significantly risen. Users expect video search engines to be useful as an instrument that facilitates communication, education, entertainment and problem solving and, in relation to this, to satisfy diverse information needs. A user's information need is the lack that a user is attempting to overcome by engaging in information seeking behavior and can be seen as having two important dimensions: it comprises both a 'what' dimension reflecting the topic of the search and a 'why' dimension corresponding to the user intent, the immediate reason, purpose or goal behind the information need. Video search engines are relatively successful at returning search results that users find to be on topic. These results do not, however, completely satisfy the users' information needs unless they also fulfill the users' intents. The purpose of this thesis is to enable the intent-related focus shift in the design and realization of video search engines and to advance them in terms of user intent in order to satisfy users' information needs to their full extent. This advancement is challenging because it affects the entire pipeline of the video search engine: video indexing, query processing, and search results ranking. However, it also has the potential to substantially improve the overall utility of video search engines and increase the impact, significance and economic value of the online video content. We start to tackle this challenge by analyzing a real-world transaction log produced by a state-of-the-art video search engine with the objective to obtain a deeper understanding why queries submitted by users in their search sessions fail. Based on the results of this analysis, we build classifiers to automatically predict these reasons for query failure given a set of multimodal features derived from both the user interactions with the search engine as well as the search results produced by the engine. Our analysis of the transaction log reveals several distinct reasons for why user queries in video search fail. Among others, one of the reasons is the way user goals are expressed in the query, i.e., a single query can correspond to different underlying goals. In other words, intent is often not explicitly reflected in the query. This fact motivates us to tackle this challenge and to investigate the usefulness of incorporating user intent in video search engines. As a first step, we investigate the nature of the immediate reason, purpose or goal behind a user information need that constitutes intent. We carry out a social-Web mining approach combined with crowdsourcing and a manual coding process in order to derive a conceptual model (i.e., a typology constituting search intent categories) covering different reasons why users consult video search engines. This typology builds the basis for integrating user intent in video search engines. We then provide evidence that users differentiate videos in search engine results lists on the basis of these user intent categories. In addition to understanding which search intents exist in video search, it is equally important to understand which intents are associated with videos. This understanding is crucial, as it builds the foundation of matching the search intents expressed by users in search scenarios to the intents that are associated with videos stored in the video search engine's index. While in search scenarios intent can be characterized by the different reasons why users consult a video search engine, comparable user actions can be investigated for why videos were added to the search engine's index in the first place. For this reason, we investigate the user action of uploading videos to the Internet and apply a combination of social-Web mining and crowdsourcing to arrive at a conceptual model (i.e., a typology constituting uploader intent categories) that characterizes the various reasons why users upload videos to the Internet. We then build algorithms that automatically classify videos into these categories. Finally, we demonstrate that uploader intent categories correlate with search intent categories, which provides the opportunity for incorporating intent into the retrieval functions of video search engines. With search intent categories and uploader intent categories and their automatic prediction at hand, we face the challenging task of introducing user intent in search results rankings that produce video search results lists that optimally reflect user intent. We propose an intent-aware video search result optimization approach that exploits the structure of topically-relevant initial results lists produced by the search engine in response to user-submitted queries in order to predict which search intent/s the user would most likely wish to satisfy. Based on this information, the approach optimizes the initial lists in a way that search results with the highest potential to satisfy the users' search intent/s are positioned at the very top of the list without decreasing its topical focus. Finally, although this thesis contributes a substantial amount of research towards user intent-aware video search engines, we believe that additional challenges will emerge in the future that will go above and beyond the challenges addressed in this thesis. We identify and discuss these challenges and expect them to attract significant research efforts that will lead to productive outcomes in the field of user intent-aware video search engines in the following years.