A Learning Auction Mechanism for Video Streaming Transmission with Decoupling Uplink-Downlink Association

In cellular networks, the video streaming services have been suffered from the limited network resources. For improving the video streaming services, decoupled uplink-downlink (DUDe) and multicast transmission with non-orthogonal multiple access (NOMA) are introduced to improve transmission efficiency. DUDe is considered beneficial to heterogeneous networks, which enable users to associate with different base stations (BSs) in uplink and downlink, respectively. Multicast transmission with NOMA is introduced to exploit the potential of multicast transmission, which enhances the efficiency of resource utilization. Combining the advantages of DUDe and multicast transmission with MOMA, we propose an auction mechanism to efficiently allocate resources and maximize the social welfare. However, the complex relationship between user value and resource configurations presents nonlinearity and non-convexity in the optimization problem. When calculating the user value in video streaming, most existing works do not consider the differences in individual user preferences. To address the problem, deep learning is utilized to model user value and transform the social welfare optimization problem into a mixed integer programming problem for solving efficiently. Our experiments show that the proposed mechanism is effective in heterogeneous networks.