Hit Ratio Driven Mobile Edge Caching Scheme for Video on Demand Services
This work addresses bandwidth and delay issues for video streaming users, but it is incremental as it builds on existing caching optimization methods.
The paper tackles the cache placement problem in mobile edge computing systems for video on demand services by formulating it as a grouping knapsack problem and solving it with a dynamic programming algorithm, resulting in a greatly improved cache hit ratio as shown in simulations.
More and more scholars focus on mobile edge computing (MEC) technology, because the strong storage and computing capabilities of MEC servers can reduce the long transmission delay, bandwidth waste, energy consumption, and privacy leaks in the data transmission process. In this paper, we study the cache placement problem to determine how to cache videos and which videos to be cached in a mobile edge computing system. First, we derive the video request probability by taking into account video popularity, user preference and the characteristic of video representations. Second, based on the acquired request probability, we formulate a cache placement problem with the objective to maximize the cache hit ratio subject to the storage capacity constraints. Finally, in order to solve the formulated problem, we transform it into a grouping knapsack problem and develop a dynamic programming algorithm to obtain the optimal caching strategy. Simulation results show that the proposed algorithm can greatly improve the cache hit ratio.