Digital data hiding is a technology being developed for multimedia services, where significant amounts of secure data is invisibly hidden inside a host data source by the owner, for retrieval only by those authorized. The hidden data should be re-coverable even after the host has undergone standard transformations, such as compression. In this paper, we present a source and channel coding framework for data hiding, allowing any tradeoff between the visibility of distortions introduced, the amount of data embedded, and the degree of robustness to noise. The secure data is source coded by vector quantization, and the indices obtained in the process are embedded in the host video using orthogonal trans-form domain vector perturbations. Transform coefficients of the host are grouped into vectors and perturbed using noise-resilient channel codes derived from multidimensional lattices. The perturbations are constrained by a maximum allowable mean-squared error that can be introduced in the host. Channel-optimized can be used for increased robustness to noise. The generic approach is readily adapted to make retrieval possible for applications where the original host is not available to the retriever. The secure data in our implementations are low spatial and temporal resolution video, and sampled speech, while the host data is QCIF video. The host video with the embedded data is H.263 compressed, before attempting retrieval of the hidden video and speech from the re-constructed video. The quality of the extracted video and speech is shown for varying compression ratios of the host video.