从QoS分布到QoE分布：系统的观点
From QoS Distributions to QoE Distributions: a System's Perspective
摘要：
在QoE管理的上下文中，网络和服务提供商通常依赖于映射系统QoS条件的模型（例如，系统响应时间、数据包丢失等）到估计的终端用户QoE值。可以假设系统中的可观察QoS条件遵循特定分布，这意味着不同的终端用户将经历不同的条件。另一方面，从主观用户研究的结果，我们知道，用户的多样性导致任何给定的测试条件（在这种情况下，指的是感兴趣的QoS参数）的用户分数的分布。我们先前的研究已经表明，为了正确地导出各种QoE度量（例如，平均意见得分（MOS）、分位数、用户评级为“良好或更好”的概率等）在给定条件下的系统中，需要考虑从用户研究中获得的评级分布，而这些分布通常是不可用的。在本文中，我们扩展这些发现，以显示如何近似用户评级分布给定的QoS到MOS映射函数和二阶统计。然后，可以将这样的用户评级分布与在系统中观察到的QoS分布组合，以最终导出QoE分数的对应分布。我们提供了两个示例来说明该过程：1）使用将等待时间与QoE相关联的Web QoE模型的分析结果，以及2）使用将分组丢失与视频停顿模式相关联的测量的数值结果，其进而被映射到QoE估计。本文中的结果提供了一个解决方案，以了解系统中的QoE分布的问题，在必要的数据不能直接以超出MOS的模型的形式提供的情况下，或者主观实验的全部细节不可用。
英文摘要：
In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating "good or better", etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates. The results in this paper provide a solution to the problem of understanding the QoE distribution in a system, in cases where the necessary data is not directly available in the form of models going beyond the MOS, or where the full details of subjective experiments are not available.
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