As a result, they suffer from much lower ratio and much higher latency for the vehicular node to receive all content data successfully. According to a survey in Reference [22], as the ratio of selfish node rises from 0 to 40%, the number of packet losses increases 500%. For alleviating this defect, three types, that is, credit-based [23; 24; 25; 26; 27; 28; 29; 30; 31], reputation-based [32; 33] and tit-for-tat (TFT)-based [34; 35], of incentive schemes have been proposed to encourage the node to share their contents. In this paper, we focus on the credit-based incentive scheme for stimulating the vehicular node to re-broadcast the content data it received to its neighbors by paying some credits to it. The credit-based schemes introduce the concept of credits to regulate the packet forwarding process among nodes. For each packet-forwarding operation, an authority would charge the sending node of the packet a unit of credit and pay it to each relay node. It is well known that the central control system (CCS) to pay the credit among nodes becomes the major problem if the CCS fails [23; 24; 25; 26]. Moreover, each credit-based work has defects on its own incentive mechanism. For example, RCM [25] suffers the overspending problem, which means that it cannot estimate the number of forwarding nodes ahead such that it fails to limit whole payments. FRAME [26] may pay too fewer rewards to a node in some cases, which lowers the incentive of the node to forward packets. Most importantly, these credit-based incentive protocols work independently with the SLNC-basedCCD approach, which means rare credit-based incentive protocol can stimulate all kinds of nodes to accelerate the SLNC-based CCD process. Based on our prior work [36], we propose SocialCode (SC) that has subsequent contributions. 1. To solve the aforementioned defect that traditional SLNC-based CCD schemes do not realize the difference of network-coded content pieces among vehicular nodes, SC designs the content bitmap to store which network-coded content pieces every node holds. Whenever SC begins its CCD operations, every node broadcasts the beacon message to interchange its content bitmap with its neighbors. In this way, each node is able to compute the number of distinct network-coded content pieces, the re-broadcast priority and the number of shared SLNC content pieces after re-encoding. 2. Based on distinct levels of interests and selfishness on different content data, SC first categorizes the vehicular node into four classes, that is, the destination, intermediate, irrelevant and overhearing ones and then designates their associated network-coding and re-broadcasting operations to share SLNC content pieces efficiently among these four types of nodes. 3. SC improves the conventional SLNC-based cooperative content distribution protocol as mentioned above. It further rigidly combines with the proposed credit-based incentive approach to encourage all classes of vehicular nodes to rise their incentives for sharing content data. In this paper, SC proposes its distributed flow to exchange credits among four types of nodes when they re-broadcast or receive SLNC-coded packets. Unselfish nodes increase their credits when they re-broadcast innovative content pieces to neighbors. Oppositely, selfish nodes decrease their credits as they overhear but reject to re-broadcast SLNC-coded content pieces. According to simulation results, SC stimulates these unselfish nodes for their generous content sharing but punishes selfish ones because they will run out of their credits eventually. To the best of our knowledge, SC is the first research that works on the integration of the credit-based incentive protocol and the SLNC-based cooperative content distribution. The remainder of this paper is organized as follows. Section 2 discusses related work. Section 3 explains how to classify vehicular nodes into four types and their corresponding guidelines for stimulating incentives in SocialCode. Section 4 describes designs and flows of SocialCode in detail.