quality-of-experience assessment
Large-screen 4K TVs (75 inches or larger) are experiencing a surge in popularity, providing unparalleled immersive experiences. Consequently, there has been a significant shift in user behavior towards consuming more video content on TVs. Investigating Quality of Experience (QoE) on large screens is paramount, given its critical role in upgrading the overall satisfaction and user engagement associated with video streaming services.
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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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