  Recently, implementing Radio Access Network (RAN) functionalities on
cloud-based computing platform has become an emerging solution that leverages
the many advantages of cloud infrastructure, such as shared computing resources
and storage capacity, while lowering the operational cost. In this paper, we
propose a novel caching framework aimed at fully exploiting the potential of
such Cloud-based RAN (C-RAN) systems through cooperative hierarchical caching
which minimizes the network costs of content delivery and improves users'
Quality of Experience (QoE). In particular, we consider the cloud-cache in the
cloud processing unit (CPU) as a new layer in the RAN cache hierarchy, bridging
the capacity-performance gap between the traditional edge-based and core-based
caching schemes. A delay cost model is introduced to characterize and formulate
the cache placement optimization problem, which is shown to be NP-complete. As
such, a low complexity, heuristic cache management strategy is proposed,
constituting of a proactive cache distribution algorithm and a reactive cache
replacement algorithm. Extensive numerical simulations are carried out using
both real-world YouTube video requests and synthetic content requests. It is
demonstrated that our proposed Octopus caching strategy significantly
outperforms the traditional caching strategies in terms of cache hit ratio,
average content access delay and backhaul traffic load.
