Long-term QoE Optimization in IoV Based on Cross-layer Resource Management

Considering the neglect of the long-term quality of experience (QoE) in the previous work, this paper applies a cross-layer resource management algorithm to optimize users’ long-term QoE in the Internet of vehicles (IoV). Based on the multi-hop vehicle-to-everything (V2X) communication downlink transmission system, the rate arrival and departure are designed into a stochastic queue model. Then the optimization problem is transformed to a trade-off problem between queue stability and long-term QoE, through Lyapunov optimization. Moreover, the trade-off problem is decomposed into a series of online sub-problems, which involves the joint optimization of rate control, power allocation and mobile relay selection. On one hand, the rate control problem is decoupled and solved by the Lagrangian method independently. On the other hand, a two-side matching algorithm is introduced into the joint power allocation and mobile relay selection optimization, to obtain low complexity. At last, simulation results demonstrate the queue stability and the superiority of system performance.