...
首页> 外文期刊>IEEE Transactions on Broadcasting >Efficient Group-Based Multimedia-on-Demand Service Delivery in Wireless Networks
【24h】

Efficient Group-Based Multimedia-on-Demand Service Delivery in Wireless Networks

机译:无线网络中基于组的高效按需多媒体服务交付

获取原文
获取原文并翻译 | 示例
           

摘要

Recently, an upsurge of interest has been observed in providing multimedia on-demand (MoD) services to mobile users over wireless networks. Nevertheless, due to the rapidly varying nature of mobile networks and the scarcity of radio resources, the commercial implementation is still limited. This paper presents an efficient group-based multimedia-on-demand (GMoD) service model over multicast-enabled wireless infrastructures, where users requesting the same content are grouped and served simultaneously with a single multicast stream. The grouping is fulfilled through a process named "batching". An analytical model is derived to analyse a timeout-based batching scheme with respect to the tradeoff between user blocking probability and reneging probability. Based on the deduced analytical model, an optimal timeout-based batching scheme is proposed to dynamically identify the optimal tradeoff point that maximizes the system satisfaction ratio given a particular system status. The proposed scheme is evaluated by means of simulation and compared with two basic batching schemes (timeout-based, size-based), and two hybrid ones (combined-for-profit, combined-for-loss). The simulation results demonstrate the proposed approach can ensure significant gains in terms of user satisfaction ratio, with low reneging and blocking probabilities
机译:近来,已经观察到在通过无线网络向移动用户提供多媒体点播(MoD)服务方面的兴趣激增。然而,由于移动网络的性质迅速变化以及无线电资源的稀缺,商业实施仍然受到限制。本文提出了一种在启用多播的无线基础架构上的有效的基于组的按需多媒体(GMoD)服务模型,在该模型中,将请求相同内容的用户进行分组,并通过单个多播流同时为其提供服务。通过名为“批处理”的过程完成分组。推导了一个分析模型,以分析基于超时的批处理方案,以权衡用户阻塞概率和更新概率。在推导的分析模型的基础上,提出了一种基于超时的最优批处理方案,以动态识别在特定系统状态下最大化系统满意度的最佳折衷点。通过仿真评估了所提出的方案,并将其与两种基本的批处理方案(基于超时,基于大小)和两种混合方案(获利组合,亏损组合)进行了比较。仿真结果表明,所提出的方法可以确保用户满意率的显着提高,并且具有较低的拒绝和阻止概率

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号