Resource Allocation for D2D Video Multicast Using Multi-Leader Multi-Follower Stackelberg Game With Personalized Incentives

Abstract

With the growing demand of mobile multimedia services, device-to-device (D2D) multicast provides an efficacious solution to local content sharing. The radio resource needs to be allocated efficiently to enhance the content sharing via D2D multicast. However, the existing resource allocation schemes for solving D2D multicast usually ignore the attribution problem of mobile users to multiple communities and the problem of social differentiation. Unlike the existing works, this paper performs the D2D video multicast in two steps: community formation and resource allocation. In the process of D2D multicast community formation, the best core users for content distribution in the network are selected in consideration of both physical and social factors, and detect whether there is overlap in the communities to which others belong. Then, we explore mobile users' social features including the historical request file similarity, the QoS request differentiation and the random mobility characteristic to arrange the best attribution schemes targeting at not only ensuring the quality of the video multicast service but also maximizing the content hit ratio. In the resource allocation step, a multi-agent hierarchical learning (MAHL) algorithm with personalized incentives based on multi-leader multi-follower Stackelberg game is proposed to maximize the throughput of the network. Simulations are conducted to reveal that compared with the other three benchmark algorithms, the proposed algorithm can significantly improve the throughput of the network under different scenarios. INDEX TERMS D2D video multicast, overlapping community detection, content hit ratio, resource allocation, multi-leader multi-follower Stackelberg game.

D2D Enabled Cellular Network Spectrum Allocation Scheme Based on the Cooperative Bargaining Solution

“…Recently, they have attracted lots of attention due to their various advantages. Compared to these existing SGRA, RMRA and SARA schemes [5], [10], [11], we demonstrate that the proposed scheme attains a better performance during the D2D multicasting operations.…”

“…In [5], the Stackelberg Game based Resource Allocation (SGRA) scheme is proposed to efficiently allocate the spectrum resource while enhancing the content sharing via D2D multicast. This scheme investigates the resource allocation for D2D video multicast, and formulates the distributed channel and power allocation problem as a multi-leader multi-follower Stackelberg game.…”

“…Based on the developed Stackelberg game, a multi-agent hierarchical learning algorithm is proposed to optimize the resource allocation of the entire network, which can improve the throughput of the network. Finally, the performance of SGRA scheme is validated by comparing to other three existing algorithms through simulation results [5].…”

“…With the landing of the key technology of D2D communications, D2D content sharing is known to be an effective solution to improve the quality of local services of cellular networking. Fortunately, D2D multicast content sharing can reduce the redundancy transmissions for the same contents, and making it possible to achieve the large-scale parallel implementation in a distributed manner [5]- [7].…”

“…The spectrum resource, which is assigned to the cluster head, is shared within the corresponding cluster to maximize the overall network throughput. This spectrum sharing technology potentially opens new opportunities to ensure a higher spectrum efficiency [3], [5].…”