Video fingerprints are short features extracted from a video sequence in order to uniquely identify its visual content and its replicas. By advancing a new robust fingerprinting method, the present paper takes the challenge of designing an enabler for the use of Internet as a distribution tool in cinematography. In this respect, a 2D-DWT-based robust video fingerprinting method is designed so as to address two use cases, namely the retrieval of video content from a database and the tracking of in-theater camcorder recorded video content. A set of largest absolute value wavelet coefficients is considered as the fingerprint and a repeated statistical test is used as the matching procedure. The video dataset consists of two corpora, one for each use case. The first corpus regroups 3 h of heterogeneous original content (organized under the framework of the HD3D-IIO French national project) and of its attacked versions (a total of 21 h of video content). The second corpus consists of 3 h of heterogeneous content (i.e., HD3D-IIO corpus) and of 1 h of live camcorder recorded video content (a total of 4 h of video content). The inner 2D-DWT properties with respect to content-preserving attacks (such as linear filtering, sharpening, geometric, conversion to grayscale, small rotations, contrast changes, brightness changes, and live camcorder recording) ensure the following results: in the first use case, the probability of false alarm and missed detection are lower than 0.0005, precision and recall are higher than 0.97; in the second use case, the probability of false alarm is 0.00009, the probability of missed detection is lower than 0.0036, precision and recall are equal to 0.72.