Joint pushing and caching is recognized as an efficient remedy to the problemof spectrum scarcity incurred by tremendous mobile data traffic. In this paper,by exploiting storage resources at end users and predictability of user demandprocesses, we design the optimal joint pushing and caching policy to maximizebandwidth utilization, which is of fundamental importance to mobile telecomcarriers. In particular, we formulate the stochastic optimization problem as aninfinite horizon average cost Markov Decision Process (MDP), for which theregenerally exist only numerical solutions without many insights. By structuralanalysis, we show how the optimal policy achieves a balance between the currenttransmission cost and the future average transmission cost. In addition, weshow that the optimal average transmission cost decreases with the cache size,revealing a tradeoff between the cache size and the bandwidth utilization.Then, due to the fact that obtaining a numerical optimal solution suffers thecurse of dimensionality and implementing it requires a centralized controllerand global system information, we develop a decentralized policy withpolynomial complexity w.r.t. the numbers of users and files as well as cachesize, by a linear approximation of the value function and optimizationrelaxation techniques. Next, we propose an online decentralized algorithm toimplement the proposed low-complexity decentralized policy using the techniqueof Q-learning, when priori knowledge of user demand processes is not available.Finally, using numerical results, we demonstrate the advantage of the proposedsolutions over some existing designs. The results in this paper offer usefulguidelines for designing practical cache-enabled content-centric wirelessnetworks.
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