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Streaming Delay-, Expenditure- and Quality-Balanced Video Over TV White Space

机译:在电视空白空间上流延时,支出和质量平衡的视频

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This paper develops a solution for video delivery over TV white space (TVWS) that can achieve a balanced tradeoff between expenditure, delay and quality. TVWS channels can be classified into two types, i.e., free TVWS channels (FTCs) and high-priority TVWS channels (HPCs). However, the development of this system encounters many challenges because of the time-varying TVWS available status, dynamic HPC spectrum leasing price, fluctuated data size of group of pictures, and diverse user requirements. To address the above issues, we first build a video data model and a TVWS model. Then, we formulate two optimization problems i) expenditure minimization subject to video quality requirement and target delay, and ii) delay minimization subject to video quality requirement and limited total budget, both of which are linear integer programming problems. Next, we develop online and offline algorithms for these two optimization problems. Finally, we implement a TVWS-based Video Delivery System (TVDS) based on a cognitive wireless network, which can use FTCs or lease HPCs to provide point-to-point (P2P) video data delivery service. The proposed algorithms are implemented in the TVDS, and extensive simulations are presented to verify the effectiveness of the proposed algorithms under different conditions.
机译:本文开发了一种在电视空白区域(TVWS)上的视频交付解决方案,可以在支出,延误和质量之间实现平衡的权衡。 TVWS频道可以分为两种类型,即免费TVWS频道(FTCS)和高优先级TVWS通道(HPC)。然而,由于可用的TVWS可用状态,动态HPC频谱租赁价格,图片集团的波动数据大小以及不同的用户要求,这种系统的开发遇到了许多挑战。为了解决上述问题,我们首先构建视频数据模型和TVWS模型。然后,我们制定了两种优化问题i)支出最小化对视频质量要求和目标延迟,并且II)延迟最小化,以视频质量要求和总预算总计有限,这两种是线性整数编程问题。接下来,我们在线和离线算法开发这两个优化问题。最后,我们基于认知无线网络实现基于TVWS的视频传送系统(TVDS),其可以使用FTCS或租赁HPC来提供点对点(P2P)视频数据传送服务。所提出的算法在TVDS中实现,并提出了广泛的模拟以验证所提出的算法在不同条件下的有效性。

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