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User-centric resource management strategies for IPTV services

机译:IPTV服务的以用户为中心的资源管理策略

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With the advent of higher bandwidth internet services, video over IP is becoming more common with the video transfer mechanisms currently being standardized by the Open IPTV Forum. IPTV in the home is starting to dominate the market and is set to replace the way we watch our media content from watching it when broadcast to watching it on demand. Existing home gateway solutions or access points can allocate bandwidth to users depending on traffic priority or on a first come first served basis. This however might not be an optimal choice of allocation since it does not take into account user privileges and user perceived quality. This paper proposes a Cognitive Resource Manager, which takes into account these factors for managing the resources in the home. The paper suggests methods for learning user preferences by employing simple machine learning techniques and defines a user satisfaction utility, then optimizes it, choosing the optimal configuration for resource allocation. The paper also proposes a more lightweight heuristic approach which approximates the optimal configuration for resource allocation at a lower computational cost. This method of resource allocation compares favorably to a number of alternative strategies with respect to fairness.
机译:随着较高的带宽互联网服务的出现,通过Open IPTV论坛的视频传输机制变得越来越常见。 IPTV在家中开始占据市场,并设置为在广播时观看我们的媒体内容观看媒体内容的方式。现有的家庭网关解决方案或接入点可以根据流量优先考虑或首先先获得用户分配给用户的带宽。然而,这可能不是最佳的分配选择,因为它没有考虑到用户特权和用户感知质量。本文提出了一种认知资源管理器,该管理员考虑了管理家庭资源的这些因素。本文建议通过采用简单的机器学习技术来学习用户偏好的方法,并定义用户满意实用程序,然后优化它,选择资源分配的最佳配置。本文还提出了一种更轻质的启发式方法,其近似于计算成本的资源分配的最佳配置。这种资源分配方法对若干替代策略相比,相对于公平性。

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