In many applications (like social or sensor networks) the information generated can be represented as a continuous stream of RDF items, where each item describes an application event (social network post, sensor measurement, etc). In this paper we focus on compressing RDF streams. In particular, we propose an approach for lossless RDF stream compression, named RDSZ (RDF Differential Stream compressor based on Zlib). This approach takes advantage of the structural similarities among items in a stream by combining a differential item encoding mechanism with the general purpose stream compressor Zlib. Empirical evaluation using several RDF stream datasets shows that this combination produces gains in compression ratios with respect to using Zlib alone