Constraint programming is pervasive and widely used to solve real-time problems which input data could be scaled up to the huge sizes, and the results are required to be given efficiently and dynamically. Many technologies such as constraint programming, hybrid technologies, and mixed integer programming could have different solvers and backends to solve problems. Streaming videos problem requires to decide which videos to put in which cache servers, in order to minimise the waiting time for all requests with a description of cache servers, network endpoints and videos, are given. In this paper, we model the streaming videos problem in two different ways. The first model is implemented using heuristics, and the global constraints are used in the second model. The experiments will be benchmarked using MiniZinc. The aim of the paper is to benchmark those technologies to evaluate the execution time and final scores of the two models using large instances of input data from Google Hash Code.