###### Abstract
However, the significant path loss restricts the transmission distance [14]. Moreover, the high cost of terahertz equipment poses a challenge to its widespread deployment [15]. In addressing the limited transmission range inherent to terahertz, the deployment of base stations operating in different frequency bands becomes a necessity. Fortunately, Free Space Optical Communication (FSOC) has emerged as a practical solution. It operates in the near-infrared wavelength spectrum, making it suitable for outdoor use [16]. FSOC offers cost-effective implementation, wide bandwidth, and high-speed data transmission [17]. Therefore, it can be deployed alongside terahertz base stations [18]. However, FSOC's effectiveness relies on establishing an unobstructed line-of-sight link between transmitters and receivers, limiting its use to point-to-point communication. Nevertheless, non-line-of-sight (NLOS) FSOC [19] addresses this constraint by using diffuse reflectors to disperse light, enabling communication even without a direct line of sight [20]. With the continuous growth of future network traffic, the problem of insufficient bandwidth during peak periods becomes a critical issue. There have been numerous research endeavors dedicated to bandwidth allocation within vehicular networks, aiming to adeptly tackle the intricate challenges linked to the fair distribution of network resources among vehicles. [PERSON] et al. [21] introduced a Vickrey-Clarke-Groves auction model to tackle resource scheduling challenges for time-sensitive services. They optimized bandwidth allocation for connected autonomous vehicles using a Lagrange relaxation algorithm. [PERSON] et al. [22] introduced a transaction framework based on blockchain technology for communication between vehicles and roadside units (RSUs). They employed a Stackelberg game model to establish secure and optimal bandwidth allocation and pricing strategies between RSUs and vehicles. Recent literature has witnessed a proliferation of studies presenting bandwidth allocation solutions tailored to multimedia applications for vehicle users. Several of these studies have incorporated caching techniques to mitigate latency. Furthermore, some have delved into the exploration of Space-Air-Ground-Integrated Networks, a concept that integrates satellite networks and unmanned aerial vehicles to enhance communication capabilities. To name a few, [PERSON] et al. [23] proposed a network architecture for Space-Air-Ground Internet of Vehicle Systems, which aimed to ensure quality-of-service-compliant network access for vehicles. They introduced a deployment scheme capable of adapting Access Assistant positions in response to increased network demands stemming from a higher number of vehicles. This scheme also incorporated vehicle information caching to facilitate access decision-making. [PERSON] et al. [24] introduced a novel 360-degree video caching and delivery framework tailored to advanced edge-enhanced wireless networks. They presented an edge cooperative caching scheme based on multi-agent reinforcement learning and introduced a two-tier base station-multicast group matching mechanism to address collaboration challenges in edge content delivery. [PERSON] [25] delved into video transcoding between different versions, with the base station considering energy consumption when caching video segments. They subsequently proposed a network resource pricing algorithm that factors in the varying usage of caching, base station computing resources, and backhaul links by vehicle users, enhancing network resource utilization flexibility. [PERSON] and [PERSON] [26] explored future 5G network-related technologies and internet of vehicle development trends. By incorporating both a 5G base RSU station and a UAV mobile base station, they bolstered network resources for scalable coded video allocation. Additionally, they derived an improved edge caching strategy through an analysis of user mobility.