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Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

  • Kiani Mehr, Sheyda1
  • Jogalekar, Prasad2
  • Medhi, Deep1
  • 1 University of Missouri–Kansas City, Kansas City, USA , Kansas City (United States)
  • 2 Ericsson, Santa Clara, USA , Santa Clara (United States)
Published Article
Journal of Internet Services and Applications
Springer London
Publication Date
Apr 26, 2021
DOI: 10.1186/s13174-021-00133-y
Springer Nature


Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

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