## 2 Related Work  
A considerable study has been conducted on the design of mobile edge caching algorithms for mobile networks. The authors in [17] proposed proactive caching of popular content during off-peak periods to reduce peak traffic demands. The authors in [18] conducted a comprehensive survey regarding different aspects of mobile edge caching. Furthermore, they have discussed caching schemes based on different caching locations and performance criteria. The work in [19; 20] leveraged edge nodes (e.g., small cells) to store popular content, such as multimedia files, to reduce latency and improve the performance of 5G networks. The authors in [21] propose a novel deep learning-based proactive caching framework in cellular networks that obtain higher backhaul offloading and user satisfaction. Along this line of mobile edge caching, video placement has been studied over different heterogeneity of networks. Considering that user devices and preferences and needs for specific videos may vary, adaptive bitrate (ABR) streaming becomes a pivotal technique to improve the quality of delivered video on networks. The authors in [22] envision a collaborative joint caching and processing strategy for multiple bitrate video delivered to adapt to the heterogeneity of user capabilities and wireless communication conditions. According to the authors in [23] scalable video coding (SVC) based video services are considered to formulate the joint video quality selection and caching problem, to maximize vehicular user's QoE. The solutions based on SVC on a system called DASH to ensure the quality of streaming media services have been proposed in recent studies [24]. Many works focus on the optimization efficiency of video caching and reducing video delivery delay in vehicular networks. The work in [25] proposed a cooperative transmission strategy for video transmission in small-cell networks with caching. Authors in [26] investigate a problem of cooperative mobile edge caching for scalable video streaming in HetNets. Adaptive video technology is applied to popular streaming services such as YouTube, Netflix, and Youku to provide smooth streaming and improve quality, such as Microsoft smooth streaming, Adobe's HDS, and Apple's HLS [27]. These streaming services encode videos into multiple versions with discrete bitrates. The authors in [28] proposed a mechanism based on MEC to cache only the highest available bit-rate video content while converting it to the requested lower bit-rate version using the available processing power of MEC. Many recent works focus on an adaptive bitrate streaming to cope with time-varying channels incurred by vehicular users' high mobility in IoV. In [29], the authors use a technique to effectively use both ABR streaming and BS caching in vehicular networks with high channel variations. Quality of experience serves as a direct evaluation of vehicle users' experiences in mobile video transmission, and thus the authors in [30] propose a deep learning-based QoE prediction approach with a large-scale QoE dataset for mobile video transmission. The work [31] proposed to simultaneously optimize energy consumption and QoE metrics in video streaming over software-defined mobile networks (SDMN) combined with MEC. [PERSON] et al. [32] by considering the interaction between video encoding and edge caching, the authors proposed a QoE-driven cross-layer optimization scheme for secure video transmission over the backhaul links in cloud-edge networks. [PERSON] et al. [33] proposed enhancing the quality of experience-aware wireless edge caching with bandwidth provisioning in software-defined wireless networks. Latency is decreased, and the utilization of caches is improved in the proposed scheme. [PERSON] et al. [34], based on the video popularity and the wireless resource conditions of the network, proposed a joint cache allocation and video delivery scheme for the video streaming system. [PERSON] et al.