Breaking data streams into chunks, re-arranging the order in some predetermined pattern, and reforming the chunks into a new stream.  This technique minimizes the problem caused by one long gap in the data stream.  When the data is de-interleaved, the gap in the original data stream becomes many smaller gaps in the original data stream.  This type of error is less perceptible, and easier to correct.