Quality of experience‐driven resource allocation in vehicular cloud long‐term evolution networks

To improve user experience and allocate reasonably spectrum resources, the quality of experience (QoE) for user is introduced as an indicator to express the user's satisfaction with the service provided. The QoE‐driven resource allocation optimization algorithm for orthogonal frequency‐division multiple is designed to maximize the user's QoE and optimize resource allocation in vehicular cloud (VC) long‐term evolution (LTE) networks. Therein, a novel QoE model which reflects delay, transmission rate, and service price by mean opinion score (MOS) is proposed to identify different services. Furthermore, the characteristics of delay, transmission rate, and service price are also analyzed specifically at different time slots, respectively. Simulation results show that the proposed resource allocation algorithm could achieve a better QoE in the field of user satisfaction and throughput compared with other traditional algorithms, especially when the number of vehicles is greater than 30 or 22, respectively. Meanwhile, the impact of fairness on QoE and throughput are also improved apparently when the number of vehicles is small. In end, the QoE‐mode brings performance improvements on delay, transmission rate, and service price compared with other single X mode. To improve spectrum utilization efficiency and enhance the vehicle user satisfaction in vehicular LTE networks, we propose a spectrum resource allocation optimization algorithm, which is based on QoE‐driven related with delay, transmission rate, and service price. In this process, the concept of MOS as an indicator is introduced and further used to design QoE model. Meanwhile, the specific analysis on delay, transmission rate, and service price in different time slots is given out to indicate criterion of the spectrum resource obtained.