We study content update and delivery in an edge caching system consisting of a sensor, a set of users, and an aggregator. The users have an aversion to stale contents, quantified as the contents' version age cost, and they occasionally request the aggregator for updated contents. The aggregator fetches the contents from the sensor and serves the users on demand. We study optimal content fetching and serving problem, aiming to minimize the time-averaged content fetching, transmission and age costs. The problem lends itself to Markov decision problem framework albeit with a high dimensional state space due to different content ages at different users. We first consider a single user problem and derive the optimal policy. We then propose a heuristic solution to the multiuser problem, which is obtained by combining solutions to certain fictitious, augmented versions of single user problems. Finally, we numerically illustrate the derived content update and delivery policies and their properties.

We study content caching in a network consisting of a server and a base station with a finite-capacity cache. Contents are dynamic. They stochastically arrive in the system, stay for random times, and their popularities also randomly vary while they are alive. Fetching contents from the server and storing in the base station cache incurs a cost. But not having the requested contents at the base station also incurs a cost that reflects QoS deficiency. We study optimal proactive caching to minimise the time-average content miss and caching costs. We formulate this problem as an average cost Markov decision problem that is a restless multi-armed bandit problem. We argue that the problem is indexable and explicitly derives the Whittle indices. Finally, we demonstrate the efficacy of the Whittle index policy via numerical evaluation .