## 1 Introduction  
The advent of the 5G era and rapid development of cellular vehicle-to-everything (C-V2X) technologies have realized a massive number of vehicles equipped with advanced intelligent devices (e.g., high-definition (HD) players, 3D navigation equipment, vehicle mixed reality (MR) glasses), which bring significant mobile data traffic and service explosion [1]. Furthermore, the global mobile data traffic is expected to reach 77 exabytes per month by 2022, as reported by Cisco, where 79 percent of this data comes from mobile video [2]. Additionally, innovative mobile video technologies have inspired a wide range of video streaming applications in vehicular networks, becoming one of the most popular and indispensable services. Generally, a massive amount of HD mobile video data is downloaded through the backhaul network of a macro base station (MBS), resulting in congested traffic loads. Consequently, the limited backhaul bandwidth capacity poses one of the most significant challenges to HD video data delivery services in the internet of vehicles (IoV) [3]. To effectively alleviate the backhaul traffic load, reduce the content delivery latency, and meet the diverse quality of service (QoS) data transmission requirements [4], multi-access edge computing (MEC) has been proposed as an efficient paradigm [5], which enables data caching at the edge of mobile networks, thus offers fast, and video delivery. The quality of experience (QoE) of mobile video streaming is a key criterion for measuring mobile video quality in mobile networks [6]. One promising method to improve the QoE of mobile users is to bring video content close to mobile users by deploying the MEC server (MECS) in mobile edge networks. Hence, mobile users can download the requested HD video from the small base stations (SBS) directly instead of acquiring the relevant contents from the MBS through the backhaul network, which reduces transmission delay for video distribution. However, the existing mobile network edge caching strategies can hardly be applied directly in vehicular edge caching networks for the following reasons [7; 8]. First, high vehicle mobility and limited caching storage capability bring difficulties in achieving HD video delivery among vehicle users. Second, the high bitrate of the emerging Ultra HD videos (e.g., 1080p, 2K, and 4K video that request at least 12-25 Mbps user data rate [9]) may incur vehicle users to be unable to complete HD video download tasks within service hours of road side unit (RSU). Owing to the limited backhaul capacity, the MBS, which took over the service, can hardly provide bitrate HD video requirements, leading to failure to download tasks. Additionally, considering the high vehicle mobility, the video transmission time is limited by the time duration a vehicle is within the signal coverage of an RSU [10]. As a result, the traditional mobile network QoE evaluation method relying on available service time is infeasible to apply vehicular caching networks directly. Adaptive bitrate (ABR) video streaming has recently become a technology to improve QoE [11]. However, many works have focused on the ABR video process, ignoring the heterogeneity of networks and user capabilities that impact ABR video caching. MECS can connect with vehicle users for high-speed and stable transmissions, but its storage capacity is limited [12]. Caching high-bitrate videos can achieve higher QoE than low-bit-rate videos, but requires higher caching resources. Hence, there is a trade-off between caching some high-bitrate videos and diverse videos [13; 14]. Moreover, the duration for downloading HD video is limited by the constrained time that a vehicle passes through the RSU service area [15]. To achieve maximum QoE, the caching decision of MECS in vehicular networks becomes challenging to support mobile videos of multiple bitrate levels [16]. Motivated by the abovementioned challenges, in this paper, we propose a deep reinforcement learning (DRL)-based intelligent caching strategy for mobile video in vehicular networks.