当前位置: X-MOL 学术IEEE Trans. Netw. Serv. Manag. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Reliable and Efficient Multimedia Service Optimization for Edge Computing-based 5G Networks: Game Theoretic Approaches
IEEE Transactions on Network and Service Management ( IF 5.3 ) Pub Date : 2020-09-01 , DOI: 10.1109/tnsm.2020.2993886
Tengfei Cao , Changqiao Xu , Junping Du , Yawen Li , Han Xiao , Changhui Gong , Lujie Zhong , Dusit Niyato

The edge computing-based 5G networks have the advantages in efficiently offloading the large-scale Internet traffic, which is considered to be a promising architecture to alleviate the conflict between transmission performance and quality of experience (QoE). However, due to the unreliability of service providers and the mutual interference between wireless channels in 5G networks, it is still difficult for existing solutions to provide satisfactory multimedia services for mobile users. In response to these crucial challenges, this paper proposes a reliable and efficient multimedia service optimization framework named “REMSO” hereby, including a two-stage joint optimization procedure. Specifically, a reliable video service mechanism is first constructed to help the mobile users distinguish the credible and economic service BSs. Afterwards, an efficient wireless resource allocation strategy is established to achieve low latency and energy efficient video service optimization. In particular, the Stackelberg and potential game models are leveraged to achieve these optimization objectives. Finally, extensive simulations corroborate that our REMSO framework can deliver prominent performance advantages in terms of the reliability and efficiency when comparing with the state-of-the-art solutions.

中文翻译:

基于边缘计算的 5G 网络的可靠高效多媒体服务优化:博弈论方法

基于边缘计算的 5G 网络在高效卸载大规模互联网流量方面具有优势,被认为是缓解传输性能和体验质量(QoE)之间冲突的一种很有前景的架构。然而,由于服务提供商的不可靠性以及5G网络中无线信道之间的相互干扰,现有的解决方案仍然难以为移动用户提供满意的多媒体服务。针对这些关键挑战,本文提出了一种名为“REMSO”的可靠高效的多媒体服务优化框架,包括两阶段联合优化过程。具体而言,首先构建可靠的视频服务机制,帮助移动用户区分可信和经济的服务基站。然后,建立高效的无线资源分配策略以实现低延迟和节能的视频服务优化。特别是,利用 Stackelberg 和潜在博弈模型来实现这些优化目标。最后,广泛的模拟证实,与最先进的解决方案相比,我们的 REMSO 框架可以在可靠性和效率方面提供显着的性能优势。
更新日期:2020-09-01
down
wechat
bug