  In multi-access edge computing (MEC) systems, there are multiple local cache
servers caching contents to satisfy the users' requests, instead of letting the
users download via the remote cloud server. In this paper, a multi-cell content
scheduling problem (MCSP) in MEC systems is considered. Taking into account
jointly the freshness of the cached contents and the traffic data costs, we
study how to schedule content updates along time in a multi-cell setting.
Different from single-cell scenarios, a user may have multiple candidate local
cache servers, and thus the caching decisions in all cells must be jointly
optimized. We first prove that MCSP is NP-hard, then we formulate MCSP using
integer linear programming, by which the optimal scheduling can be obtained for
small-scale instances. For problem solving of large scenarios, via a
mathematical reformulation, we derive a scalable optimization algorithm based
on repeated column generation. Our performance evaluation shows the
effectiveness of the proposed algorithm in comparison to an off-the-shelf
commercial solver and a popularity-based caching.
