Searching relevant information based on content features in video databases is an interesting and challenging research topic that has drawn lots of attention recently. Video similarity search has many practical applications such as TV broadcast monitoring, copyright compliance enforcement and search result clustering, etc. However, existing studies are limited to provide fast and accurate solutions due to the diverse variations among the videos in large collections. In this thesis, we introduce the database support for effective and efficient video similarity search from various sources, even if there exists some transformation distortion, partial content re-ordering, insertion, deletion or replacement. Specifically, we focus on processing two different types of content-based queries: video clip retrieval in a large collection of segmented short videos, and video subsequence identification from a long unsegmented stream. The first part of the thesis investigates the problem of how to process a number of individual kNN searches on the same database simultaneously to reduce the computational overhead of current content-based video search systems. We propose a Dynamic Query Ordering (DQO) algorithm for efficiently processing Batch Nearest Neighbor (BNN) search in high-dimensional space, with advanced optimizations of both I/O cost and CPU cost. The second part of the thesis challenges an unstudied problem of temporal localization of similar content from a long unsegmented video sequence, with extension to identify the occurrence of potentially different ordering or length with respect to query due to video content editing. A graph transformation and matching approach supported by the above BNN search is proposed, as a filter-and-refine query processing strategy to effectively but still efficiently identify the most similar subsequence. The third part of the thesis extends the method of Bounded Coordinate System (BCS) we introduced earlier for video clip retrieval. A novel collective perspective of exploiting the distributional discrepancy of samples for assessing the similarity between two video clips is presented. Several ideas of non-parametric hypothesis tests in statistics are utilized to check the hypothesis whether two ensembles of points are from a same distribution. The proposed similarity measures can provide a more comprehensive analysis that captures the essence of invariant distribution information for retrieving video clips. For each part, we demonstrate comprehensive experimental evaluations, which show improved performance compared with state-of-the-art methods. In the end, some scheduled extensions of this work are highlighted as future research objectives.