In this work, we describe a problem which we refer to as the \textbf{Spotify problem} and explore a potential solution in the form of what we call corpus-compressed streaming schemes. Inspired by the problem of constrained bandwidth during use of the popular Spotify application on mobile networks, the Spotify problem applies in any number of practical domains where devices may be periodically expected to experience degraded communication or storage capacity. One obvious solution candidate which comes to mind immediately is standard compression. Though obviously applicable, standard compression does not in any way exploit all characteristics of the problem; in particular, standard compression is oblivious to the fact that a decoder has a period of virtually unrestrained communication. Towards applying compression in a manner which attempts to stretch the benefit of periods of higher communication capacity into periods of restricted capacity, we introduce as a solution the idea of a corpus-compressed streaming scheme. This report begins with a formal definition of a corpus-compressed streaming scheme. Following a discussion of how such schemes apply to the Spotify problem, we then give a survey of specific corpus-compressed scheming schemes guided by an exploration of different measures of description complexity within the Chomsky hierarchy of languages.