Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective video broadcasting always remains a difficult task. In this work, we proposed a quality of video visualization guarantee model based on a feedback loop and an efficient algorithm for segmenting and replicating video segments using the Payoff-based Dynamic Segment Replication Policy (P-DSR). In the urban VANET environment, P-DSR is defined by taking into account the position of the vehicles, the speed, the direction, the number of neighboring vehicles, and the reputation of each node to stabilize the urban VANET topology. However, the management of various load control parameters between the different components of the urban VANET network remains a problem to be studied. This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input. This algorithm aims to provide an improved class lifetime, an improved video segment delivery rate, a reduced inter-class overload, and an optimization of a global criterion. A scalable algorithm is used to optimize the parameters of the GCMAV. The simulations were carried out using the NetSim simulator and Multi-Objective Evolutionary Algorithms framework to optimize parameters. Experiments were carried out with realistic maps of Open Street Maps and its results were compared with other algorithms such as Seamless and Authorized Multimedia Streaming and P-DSR. The survey suggests that the proposed methodology works well concerning the average lifetime of the inter-classes and the delivery rate of video segments.

中文翻译：

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适应Urban VANET的具有属性向量的基于Payoff的动态分段复制和图分类方法

由于约束的数量和车载自组织网络 (VANET) 的动态特性，有效的视频广播始终是一项艰巨的任务。在这项工作中，我们提出了一种基于反馈循环的视频可视化质量保证模型和一种使用基于支付的动态片段复制策略（P-DSR）分割和复制视频片段的有效算法。在城市 VANET 环境中，P-DSR 是通过考虑车辆的位置、速度、方向、相邻车辆的数量以及每个节点的信誉来定义的，以稳定城市 VANET 拓扑。然而，城市VANET网络不同组件之间各种负载控制参数的管理仍然是一个有待研究的问题。这项工作使用了一个多目标问题，该问题将我们基于属性向量图分类方法 (GCMAV) 的算法的参数作为输入。该算法旨在提供改进的类生存期、改进的视频片段传递率、减少的类间过载和全局标准的优化。可扩展算法用于优化 GCMAV 的参数。使用 NetSim 模拟器和多目标进化算法框架进行模拟以优化参数。使用 Open Street Maps 的真实地图进行了实验，并将其结果与其他算法进行了比较，例如无缝和授权多媒体流和 P-DSR。