In this project, we are interested with wireless scheduling algorithms for the downlink of a single cell that can minimize the queue overflow probability. In particular, in a large deviation setting, we are interested in algorithms that maximize the asymptotic decay rate of the queue overflow probability, as the queue overflow threshold approaches infinity. We initially infer an upper bound on the decay rate of the queue overflow probability overall scheduling policies. We then focus on a class of scheduling algorithms collectively referred to as the “α-algorithms.” For a given α ≥ 1, the α-algorithm picks the client for the benefit at each time that has the largest product of the transmission rate multiplied by the backlog raised to the power α. We demonstrate that when the overflow metric is appropriately modified, the minimum cost-to-flood under the α-calculation can be achieved by a simple linear path, and it can be written as the solution of a vector-optimization issue. Utilizing this structural property, we at that point demonstrated that when α approaches infinity, the α-algorithms asymptotically accomplish the largest decay rate of the queue. At long last, this result enables us to configuration scheduling algorithms that are both near ideal as far as the asymptotic decay rate of the overflow probability and experimentally appeared to keep up little queue flood probabilities over line length scopes of useful intrigue.