quality-of-experience assessment
As communications service providers ponder ways to cater to the diverse traffic requirements of mobile applications that range from the classic telephony to modern augmented reality (AR)-related use cases, the traditional quality of service (QoS)-based radio resource management (RRM) techniques for RAN slicing that are agnostic to the intrinsic workings of applications can result in a poor quality of experience (QoE) for the end-user. We argue that in addition to QoS, RAN slicing strategies should also consider QoE for efficient resource utilization.
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The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models.
Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices.
We collect human opinions for each video with a series of carefully designed subjective experiments.
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