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A Pre-caching Mechanism of Video Stream Based on Hidden Markov Model in Vehicular Content Centric Network

机译:车辆内容中心网络中隐马尔可夫模型的视频流预缓存机制

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Vehicular Content Centric Network (VCCN) enjoys advantages in effectiveness and convenience for content distribu- tion among vehicles, by adopting features of Content Centric Net- works into Vehicular Ad-Hoc Networks. However, the dynamic topology makes it difficult to establish stable connections. To get a better network performance and user experience, we exploit the user mobility to achieve efficient content distribution in VCCN. In this paper, we propose a mechanism for pre-caching chunks of large content objects such as videos among RSUs. First, we adopt Hidden Markov Model (HMM) to predict a user’ s moving trajectory. Based on the time gap from the current location to the predicted location of the mobile user, the corresponding RSU can pre-cache the required video chunks and provide them to the user as soon as he arrives at the predicted location. Simulation results show that our scheme is effective with higher cache hit, lower deliver latency, lower deliver overhead and lower average hops compared to other pre-caching schemes.
机译:车辆内容中心网络(VCCN)通过采用内容中心网的特征在车辆ad-hoc网络中采用内容分配,在车辆中的有效性和便利性方便的优势。但是,动态拓扑使得难以建立稳定的连接。为了获得更好的网络性能和用户体验,我们利用用户移动性以在VCCN中实现高效的内容分发。在本文中,我们提出了一种机制,用于预先缓存大型内容对象的大块,例如RSU之间的视频。首先,我们采用隐藏的马尔可夫模型(HMM)来预测用户的移动轨迹。基于从当前位置到移动用户预测位置的时间间隙,相应的RSU可以预先缓存所需的视频块,并一旦他到达预测位置就会向用户提供给用户。仿真结果表明,与其他预缓存方案相比,我们的方案较高的缓存命中,降低交付延迟,降低交付开销和较低的平均跳跃。

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