Random Caching Optimization in Large-Scale Cache-Enabled Internet of Things Networks

The introduction of cache can reduce unnecessary traffic load and improve latency in the wireless access networks, especially for wireless video broadcasting. But how cache impacts video broadcasting with scalable video coding (SVC) is still an open problem. In this paper, we analyze the optimization of random caching in a large-scale cache-enabled Internet of Things networks. Specifically, we propose to optimize the random caching strategy that aims to maximize the successful transmission probability (STP) of the video contents at edge base stations (BSs). To this end, by using the stochastic geometry theory, we derive analytical expression of STP by considering SVC to satisfy different levels of quality of service requirements. We develop a gradient-based iterative algorithm to search the local optimal solution for the general random caching strategy optimization problem. The asymptotical optimal caching strategy is obtained with a lower complexity. The closed-form STP is also obtained in high signal-to-noise ratio and particular cache size. Based on the closed-form STP expressions, the random caching strategy, i.e., caching probability of different video contents, is further optimized to enhance STP performance. Compare to different reference schemes, the proposed caching strategy improves the STP up to 18% and 21.6% with the low density of BSs and the high density of BSs, respectively.