Non-asymptotic multicast throughput capacity in multi-hop wireless networks

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks, which can explore the general scaling laws of throughput capacity but cannot predict the exact achievable throughput. In this paper, we investigate the non-asymptotic capacity of multihop wireless networks for multicast applications wherein for each source node, k nodes are randomly selected as receivers. Since multicast routing has a dynamic nature, it is challenging for the exact performance analysis. To tackle the problem, we propose an explicit analytical model which describes multicast transmissions, considers networks of arbitrary size, takes data burst into account, and also covers the notion of time scales for transient analysis. By developing a practical multicast scheme, stochastic network calculus is employed for the exact analysis. With the analytical model, we derive lower and upper bounds on multicast capacity, which are non-asymptotic functions of the above variables, and also recover the scaling laws from an asymptotic point of view. Simulations further verify the accuracy of the analytical bounds.