  In this paper, storage efficient caching based on time domain buffer sharing
is considered. The caching policy allows a user to determine whether and how
long it should cache a content item according to the prediction of its random
request time, also referred to as the request delay information (RDI). In
particular, the aim is to maximize the caching gain for communications while
limiting its storage cost. To achieve this goal, a queueing theoretic model for
caching with infinite buffers is first formulated, in which Little's law is
adopted to obtain the tradeoff between the hit ratio and the average buffer
consumption. When there exist multiple content classes with different RDIs, the
storage efficiency is further optimized by carefully allocating the storage
cost. For more practical finite-buffer caching, a $G/GI/L/0$ queue model is
formulated, in which a diffusion approximation and Erlang-B formula are adopted
to determine the buffer overflow probability and the corresponding hit ratio.
The optimal hit ratio is shown to be limited by the demand probability and
buffer size for large and small buffers respectively. In practice, a user may
exploit probabilistic caching with random maximum caching time and arithmetic
caching without any need for content arrival statistics to efficiently harvest
content files in air.
