Evaluation of an ant colony optimization based schedulerfor the transmission of multimedia traffic in the 802.11e edca

Abstract

The transmission of time-sensitive multimedia traffic across unreliable wireless network links requires adaptive scheduling that respects substantial resource fluctuations and irregular traffic arrival patterns. As finding a near-optimal adaptive transmission schedule, in our case for the prioritized access categories of the 802.11e Enhanced Distributed Channel Access (EDCA), is a combinatorial optimization problem, we introduce an Ant Colony Optimization (ACO) based meta heuristic for scheduling multimedia traffic. ACO is known to develop near-optimal solutions at the expenses of additional administrative and computational overhead. We compare ACO scheduling to a deterministic scheduling approach specifically designed for the needs of multimedia scheduling called MLLF. The aim of this paper is to evaluate the feasibility of ACO approaches for multimedia scheduling in different scenarios and to conclude on the effectiveness of the selected deterministic MLLF approach in comparison to a near-optimal solution.