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Summary form only given. Distributed source coding has emerged as an enabling technology for sensor networks. It refers to the compression of correlated signals captured by different sensors which do not communicate between themselves. Distributed source coding finds its foundation in the seminal work of Slepian-Wolf (1973) and Wyner-Ziv (1976). The proof of the Slepian-Wolf W theorem is based on random binning, which is non-constructive, i.e., it does not reveal how practical code design should be done. In 1974, Wyner suggested the use of parity check codes to approach the corner points of the Slepian-Wolf rate region. It is only recently that practical solutions based on channel capacity-achieving codes (block codes, turbo codes or LDPC codes) have been explored for applications ranging from video compression, resilient video transmission, to minimization of transmit energy in sensor networks. Video compression, as well as scalable video compression, can be recast into a distributed source coding framework leading to distributed video coding schemes targeting mainly low coding complexity and error resilience functionalities. Correlated samples (pixels or transform coefficients) from different frames are regarded as outputs of different sensors. However, the application of the Wyner-Ziv principles to video compression is not straightforward and requires solving a number of issues. This article presents the underlying theory, the latest developments of distributed video compression and some of the research trends in the area.