## 2 Traditional Video Application Methods over CVIS  
Video applications play a very important role in vehicle driving assistance, vehicle safety applications and entertainment applications. Due to the intelligence of the roadside infrastructure in CVIS, it is equipped with information storage units, computing units, etc., which can interact with vehicles in real time, and greatly promotes the development of video applications. In recent years, many researchers have conducted in-depth research on the use of video applications over CVIS and have proposed a considerable number of video transmission methods and video target detection methods. Figure 1: Video application methods over CVIS. Modern video coding standards can be divided into the following categories: H.264/AVC, H.264/SVC, H.265/HEVC, AVS2 and AVI [3; 4; 5]. At present, the encoder optimization algorithm based on the H.26X standard is widely studied, and it can reduce the video freeze time and computational complexity under the condition of ensuring video quality in complex CVIS environments. The extensible modulation (s-mod) scheme proposed by [PERSON] et al. [6] ensures the performance while significantly reducing the complexity of the encoder optimization algorithm. [PERSON] et al. [7] reduced the computational complexity through unequal packet loss protection and rate control algorithms for scalable video coding. [PERSON] et al. [8] proposed a video layering scheme based on the H.265/SVC standard to improve the average offload traffic. Forward error correction (FEC) is widely used in video coding, and can increase the credibility of data communication. Generally speaking, it causes a waste of channel resources when the receiver has no right to request retransmission in unidirectional communication after an error occurs in the transmission data. FEC is an error control mode which encodes according to an algorithm set in advance, before the data transmission, and adds redundant code information with its own characteristics. When an error occurs in the data transmission, the receiver decodes the received data stream according to the corresponding algorithm and determines the resulting error code to correct it. [PERSON] et al. [9] used RaptorQ code as an FEC scheme to reduce data packet loss and improve transmission performance. [PERSON] et al. [10] proposed an enhanced adaptive sub-packet forward error correction (EASP-FEC) video stream transmission scheme which aims to improve the quality of video transmissions over CVIS. To minimize video distortion, [PERSON] et al. [11] proposed an adaptive truncation hybrid automatic repeat request (ATHARQ) algorithm to find hierarchical video packet scheduling for interlayer forward error correction (IL-FEC) coding. Resources can be divided into network resources and cache resources based on different standards. According to the different forms of resources, the means of resource allocation and scheduling will be different. The cluster methods allocate video resources to vehicles with the same demand characteristics. Aiming to minimize video interruption probability, [PERSON] et al. [12] showed a video resource decision scheme based on a cluster coverage and fast handoff mobile IP system (COMPP). [PERSON] et al. [13] divided mobile vehicles into cooperation clusters to improve the quality of service (QOS) and quality of experience (QOE), and proposed a V2V cooperative communication method. The main joint DSRC/LTE communication method is that vehicles are allocated a corresponding communication channel according to different needs. [PERSON] et al. [14] proposed a hybrid communication method, based on DSRC/LTE, which improves the overall reliability of communication. [PERSON] et al. [15] proposed a service-aware wireless access technology (RAT) selection algorithm to improve performance. The multipath transmission method means allocating video sub-resources to different links for transmission. [PERSON] et al.