Near-Optimal Layer Placement for Scalable Videos in Cache-Enabled Small-Cell Networks

We investigate the near-optimal video layer placement in cache-enabled small-cell networks to provide on-demand video services with differential perceptual experiences. By employing scalable video coding, each video file is encoded into multiple layers, which are then stored in the local cache of small-cell base stations (SBSs). Based on the proposed caching scheme, we define the concept of average offloaded traffic (AOT), which quantitatively indicates the total amount of data traffic that can be sourced from the local cache of SBSs. Relying on stochastic geometry, we derive the expression for the successful transmission probability, based on which the closed form of AOT is obtained. Afterwards, under the cache size constraint of each SBS, the AOT maximization problem is formulated. Due to the NP-hard property of this integer programming problem, it is first relaxed into the convex form. Then, a greedy-strategy based heuristic algorithm is proposed, which can yield the near-optimal layer caching indicators. Numerical results validate our derivations and demonstrate that the proposed layer caching scheme outperforms the benchmark in terms of the AOT performance.