Joint Performance-Resource Optimization for Improved Video Quality in Fairness Enhanced HetNets

Achieving high Quality of Service (QoS) is one of the important goals in the latest 5G Heterogeneous Networks (HetNets) environments. However, ensuring fairness among users with Reduced Power Consumption (RPC) is a major challenge. Although several studies have examined the joint issue of User Association (UA), Resource Allocation (RA), and Power Allocation (PA), there is still no optimal solution that achieves QoS fairness and RPC with low complexity and processing time. This paper proposes the Power-Performance Efficient Adaptive Genetic Algorithm (P²EAGA) for solving the UA-RA-PA problem in HetNets. The UA-RA sub-problem is formulated as a ‘0/1’ Multiple Knapsack Problem (MKP) with constraints on the maximum capacity of Base Stations (BSs) along with the transport block size index. Its sub-optimal solution is given as input to the second MKP, formulated to solve the PA sub-problem with constraints on the total BS power capacity. Simulation results show that P²EAGA outperforms existing schemes in terms of variability, fairness, RPC, and QoS, including throughput, packet loss ratio, delay, and jitter. Simulation results also show that P²EAGA generates solutions that are very close to the optimal global solution compared to the Default Genetic Algorithm.