AI summary · arXiv: 2210.05444v1 · eess.IV

Modeling of Energy Consumption and Streaming Video QoE using a Crowdsourcing Dataset

Christian Herglotz, Werner Robitza, Matthias Kränzler, André Kaup, Alexander Raake

Original paper · DOI Submitted to arXiv on 11 Oct. 2022 Proc. 2022 14th International Conference on Quality of Multimedia Experience (QoMEX) 6 pages, 3 figures License: NONEXCLUSIVE-DISTRIB 1.0

The essentials

  • Significant increase in demand for online video services
  • Production and use of devices for online video tasks contribute to global greenhouse gas emissions
  • Need for research on power consumption and energy efficiency of devices during video streaming
  • Over-the-top providers striving to offer high-quality streaming experiences
  • Energy consumption and Quality of Experience (QoE) depend on the same system parameters
  • Analysis conducted on end-user power efficiency and QoE in a video streaming service
  • Crowdsourced dataset used to estimate power consumption and perceived quality
  • Intelligent choices regarding streaming parameters can optimize both QoE and power efficiency
  • Importance of considering both power efficiency and QoE when designing and evaluating video streaming services

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: Video Streaming QoE Power Efficiency Sustainable Use Crowdsourced Dataset

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  • Original abstract
In the past decade, there has been a significant increase in the demand for online video services. Recent studies have revealed that production and use of devices for online video tasks contribute to more than 1% of global greenhouse gas emissions, highlighting the need for research on power consumption and energy efficiency of these devices during video streaming to ensure sustainable use. Over-the-top providers are striving to offer high-quality streaming experiences that meet user expectations. It is important to note that both energy consumption and Quality of Experience (QoE) depend on the same system parameters; therefore, it is necessary to take a joint view when evaluating these factors. In this paper, the authors conduct an analysis of both end-user power efficiency and QoE in a video streaming service. They utilize a crowdsourced dataset consisting of 447,000 streaming events from YouTube to estimate both power consumption and perceived quality. The power consumption is modeled based on previous work which has been extended to predict the power usage of different devices and codecs. The user-perceived QoE is estimated using a standardized model. The results indicate that making intelligent choices regarding streaming parameters can optimize both QoE and power efficiency for end-user devices; suggesting users can enjoy high-quality streaming experiences while minimizing their device's energy consumption by selecting appropriate settings. Additionally, the paper discusses limitations of their approach and identifies directions for future research; addressing these limitations and exploring further avenues of investigation can continue to improve our understanding of energy consumption and QoE in video streaming services. Overall, this study sheds light on the importance of considering both power efficiency and QoE when designing and evaluating video streaming services; providing valuable insights into how these two factors can be optimized simultaneously towards more sustainable use of technology in this rapidly growing industry.

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