The maintenance of skylines in data streams is challenging because of updating non stop adding of incoming tuples and removing of expired tuples. In this paper, we present a dynamic dimension indexing based approach RSS to skyline maintenance on data streams, which is efficient at both count-based and time-based sliding windows regardless the dimensionality of data. Our analysis shows that the time complexity of RSS is bounded by a subset of the skyline and our performance evaluation shows the efficiency of RSS.