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The original coded caching scenario assumes a content library that is ofinterest to all receiving users. In a realistic scenario though, the users mayhave diverging interests which may intersect to various degrees. What happensfor example if each file is of potential interest to, say, $40\,\%$ of theusers and each user has potential interest in $40\,\%$ of the library? In thiswork, we investigate the so-called symmetrically selfish coded cachingscenario, where each user only makes requests from a subset of the library thatdefines its own File Demand Set (FDS), each user caches selfishly only contentsfrom its own FDS, and where the different FDSs symmetrically overlap to someextent. In the context of various traditional prefetching scenarios (prior tothe emergence of coded caching), selfish approaches were known to bepotentially very effective. On the other hand, with the exception of somenotable works, little is known about selfish coded caching. We here present anew information-theoretic converse that proves, in a general setting ofsymmetric FDS structures, that selfish coded caching, despite enjoying a muchlarger local caching gain and a much smaller set of possible demands,introduces an unbounded load increase compared to the unselfish case. Inparticular, in the $K$-user broadcast channel where each user stores a fraction$\gamma$ of the library, where each file (class) is of interest to $\alpha$users, and where any one specific file is of interest to a fraction $\delta$ ofusers, the optimal coding gain of symmetrically selfish caching is at least $(K- \alpha)\gamma + 1$ times smaller than in the unselfish scenario. This allowsus to draw the powerful conclusion that the optimal selfish coding gain isupper bounded by $1/(1 - \delta)$, and thus does not scale with $K$. Thesederived limits are shown to be exact for different types of demands.

Unselfish Coded Caching can Yield Unbounded Gains over Symmetrically Selfish Caching