In cross-border transactions, the transmission and processing of logistics information directly affect the trading experience and efficiency. The use of Internet of Things (IoT) technology can make this process more intelligent, efficient, and secure. However, most traditional IoT logistics systems are provided by a single logistics company. These independent systems need to withstand high computing loads and network bandwidth when processing large-scale data. Additionally, due to the complex network environment of cross-border transactions, the platform’s information security and system security are difficult to guarantee. To address these challenges, this paper designs and implements an intelligent cross-border logistics system platform that combines serverless architecture and microservice technology. This system can uniformly distribute the services of all logistics companies and divide microservices based on actual business needs. It also studies and designs corresponding Application Programming Interface (API) gateways to solve the interface exposure problem of microservices, thereby ensuring the system’s security. Furthermore, asymmetric encryption technology is used in the serverless architecture to ensure the security of cross-border logistics data. The experiments show that this research solution validates the advantages of combining serverless architecture and microservices, which can significantly reduce the operating costs and system complexity of the platform in cross-border logistics scenarios. It allows for resource expansion and billing based on application program requirements at runtime. The platform can effectively improve the security of cross-border logistics service processes and meet cross-border transaction needs in terms of data security, throughput, and latency.

With the widespread application of 5G technology, there has been a significant surge in wireless video service demand and video traffic due to the proliferation of smart terminal devices and multimedia applications. However, the complexity of terminal devices, heterogeneous transmission channels, and the rapid growth of video traffic present new challenges for wireless network-based video applications. Although scalable video coding technology effectively improves video transmission efficiency in complex networks, traditional cellular base stations may struggle to handle video transmissions for all users simultaneously, particularly in large-scale networks. To tackle this issue, we propose a scalable video multicast scheme based on user demand perception and Device-to-Device (D2D) communication, aiming to enhance the D2D multicast network transmission performance of scalable videos in cellular D2D hybrid networks. Firstly, we analyze user interests by considering their video viewing history and factors like video popularity to determine their willingness for video pushing, thereby increasing the number of users receiving multicast clusters. Secondly, we design a cluster head selection algorithm that considers users’ channel quality, social parameters, and video quality requirements. Performance results demonstrate that the proposed scheme effectively attracts potential request users to join multicast clusters, increases the number of users in the clusters, and meets diverse user demands for video quality.