## 2 Background and Related Work
Mobile edge computing (MEC) [22] and edge intelligence (EI) [23], in close proximity to the industrial manufacturing site, use the computing resources and network resources of edge gateways or servers as edge computing carriers to set up a bridge between terminal devices and cloud servers. Edge computing can reduce Industrial Internet data transmission delays, meet real-time response time requirements, reduce energy consumption, lower bandwidth costs, and solve data security and privacy issues [24]. These advantages have prompted edge computing to be widely exploited in the Internet of Things [25], Internet of Vehicles [26], and Industrial Internet [27]. At the same time, edge intelligence can also provide a method of modeling and deploying digital twins [28] for the Industrial Internet, guaranteeing the processing of large amounts of industrial flow data with low latency and high security [29] and promoting the development of intelligent manufacturing [30]. In recent years, many researchers have begun to study the combination of network coding and edge computing to solve the routing and forwarding problems in the Internet of Things network. [PERSON] et al. [31] used the characteristics of routing packet correlation on wireless links to discuss the impact of link correlation on the number of packets to be forwarded and the selection of forwarders in intelligent routing. Then, they leveraged network coding combined with opportunistic routing to improve wireless IoT. For the energy efficiency of networking infrastructure, a method based on link correlation mining for intra-session network coding for intelligent routing (NCCLC) was proposed to accurately estimate the number of transmissions required by the forwarder and select the forwarder with the optimal number of communications for routing. [PERSON] et al. [32] claimed that the REST HTTP protocol could provide WEB services in the Internet of Things. It is compatible with existing computing infrastructures and is selected as a standard protocol for IoT application communication. Their proposal combines REST HTTP with Random Linear Network Coding (RLNC) to reduce the extra packet weight consumed and to ensure reliable data exchange using a stateless REST HTTP protocol in unreliable and highly variable IoT scenarios. [PERSON] et al. built a mobile edge computing intelligent cache analysis platform, which can pre-determine which streaming media files are stored in the limited storage space of edge computing devices and provide users with an efficient, intelligent network experience. Then, the network coding and wireless relay cooperation method is used to simultaneously transmit the files extracted from the core network and the real-time regular communication network to the network's edge, thereby realizing the intelligent caching of mobile edge computing [33]. The ideas adopted in these recent studies inspired our design. However, they usually employ machine learning algorithms to facilitate the multipath forwarding of content requests. These existing strategies consume a tremendous amount of resources for model training. Although this will also be a future research direction in terms of improving the multipath forwarding capabilities, we aim to design an uncomplicated scheme that can be used in resource-constrained Industrial Internet environments.