The problem of finding interesting tuples in a data set, more commonly known as the skyline problem, has been extensively studied in scenarios where the data is static. More recently, skyline research has moved towards data streaming environments, where tuples arrive/expire in a continuous manner. Several algorithms have been developed to track skyline changes over sliding windows; however, existing methods focus on skyline analysis in which all required skyline attributes belong to a single incoming data stream. This constraint renders current algorithms unsuitable for applications that require a real-time "join" operation to be carried out between multiple incoming data streams, arriving from different sources, before the skyline query can be answered. Based on this motivation, in this paper, we address the problem of computing skyline-window-join (SWJ) queries over pairs of data streams, considering sliding windows that take into account only the most recent tuples. In particular, we propose a Layered Skyline-window-Join (LSJ) operator that (a) partitions the overall process into processing layers and (b) maintains skyline-join results in an incremental manner by continuously monitoring the changes in all layers of the process. We combine the advantages of existing skyline methods (including those that efficiently maintain skyline results over a single stream, and those that compute the skyline of pairs of static data sets) to develop a novel iteration-fabric skyline-window-join processing structure. Using the iteration-fabric, LSJ eliminates redundant work across consecutive windows by leveraging shared data across all iteration layers of the windowed skyline-join processing. To the best of our knowledge, this is the first paper that addresses join-based skyline queries over sliding windows. Extensive experimental evaluations over real and simulated data show that LSJ provides large gains over naive extensions of existing schemes which are not designed to eliminate redundant work across multiple processing layers.