Stochastic optimization for pricing-aware multimedia services in 5G vehicular networks

The many fold capacity magnification promised by 5G vehicular networks will likely provide massive multimedia services, including infotainment, augmented reality, location services, etc. However, the large-scale and stochastic characteristic of these burgeoning multimedia applications will lead to an exponential increase of traffic in vehicular networks. Meanwhile, the diversified requirements introduced by the coexistence with traditional services will also bring new challenges to the efficient usage of resources. To cope with the above challenges, we propose a novel Stochastic Optimization framework for Pricing-aware Multimedia Services (SOPMS) in this paper, which targets the maximization of utility with the constraints of system stability and traffic pricing policy. Specifically, we leverage the Lyapunov function to address this optimization objective, which is decomposed into three tractable subproblems. For each problem, a distinct algorithm is conceived, i.e. Quality of Experience (QoE) based utility maximization, cooperative resource allocation and pricing-based transmission control. Finally, validated by the simulations, our proposed SOPMS preserves the optimality and significantly improves the queue stability and service utility, in comparison with other state-of-the-art solutions.