In this paper, an optimal resource allocation scheme is proposed for random multiple access (RMA) oriented sparse code multiple access (SCMA) based vehicle-to-everything (V2X) networks. We consider an uplink RMA oriented SCMA, where vehicle-to-infrastructure (V2I) users and vehicle-to-vehicle (V2V) users are considered to randomly choose codebooks for transmission from a shared codebook pool. To avoid mutual interference between V2I users and V2V users, the total bandwidth is decomposed into two parts for transmission of V2I users and V2V users. A joint user-codebook selection and bandwidth allocation optimization problem is formulated to maximize the sum rate of V2I users while guaranteeing the reliability requirement of V2V users. This problem is a mixed-integer programming (MIP) problem with probabilistic constraint, thus it is impractical to directly solve. To solve the problem, the probabilistic constraint is converted into a non-probabilistic one by approximation. Subsequently, efficient but suboptimal staged algorithms are proposed to solve the joint optimization problem. Firstly, a new decoupled Q-learning based user-codebook selection algorithm (DQL-UCSA) is proposed to find the optimal user-codebook selection relationships, which completely address codebook collision problem. Under the optimal user-codebook selection relationships determined, the joint optimization problem is transformed into a single objective bandwidth allocation problem. Then, we solve the single objective problem using a convex optimization method, which maximizes the sum rate of V2I users. Simulation results show the DQL-UCSA can converge quickly and enable a significant performance improvement by addressing codebook collisions. Besides, the proposed RMA-SCMA with optimal bandwidth allocation (OBA) is significantly superior to conventional schemes in terms of sum rate.