Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Netflix, Amazon Prime, and YouTube: Comparative Study of Streaming Infrastructure and Strategy

  • Suman, Pandey (School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology) ;
  • Yang-Sae, Moon (Dept. of Computer Science, Kangwon National University) ;
  • Mi-Jung, Choi (Dept. of Computer Science, Kangwon National University)
  • Received : 2021.03.19
  • Accepted : 2022.10.06
  • Published : 2022.12.31
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Abstract

Netflix, Amazon Prime, and YouTube are the most popular and fastest-growing streaming services globally. It is a matter of great interest for the streaming service providers to preview their service infrastructure and streaming strategy in order to provide new streaming services. Hence, the first part of the paper presents a detailed survey of the Content Distribution Network (CDN) and cloud infrastructure of these service providers. To understand the streaming strategy of these service providers, the second part of the paper deduces a common quality-of-service (QoS) model based on rebuffering time, bitrate, progressive download ratio, and standard deviation of the On-Off cycle. This model is then used to analyze and compare the streaming behaviors of these services. This study concluded that the streaming behaviors of all these services are similar as they all use Dynamic Adaptive Streaming over HTTP (DASH) on top of TCP. However, the amount of data that they download in the buffering state and steady-state vary, resulting in different progressive download ratios, rebuffering levels, and bitrates. The characteristics of their On-Off cycle are also different resulting in different QoS. Hence a thorough adaptive bit rate (ABR) analysis is presented in this paper. The streaming behaviors of these services are tested on different access network bandwidths, ranging from 75 kbps to 30 Mbps. The survey results indicate that Netflix QoS and streaming behavior are significantly consistent followed by Amazon Prime and YouTube. Our approach can be used to compare and contrast the streaming services' strategies and finetune their ABR and flow control mechanisms.

Keywords

Acknowledgement

This research is a basic research project carried out with the support of the National Research Foundation of Korea with funding from the government (Ministry of Science and ICT) in 2020 (No. NRF-2020R1A2C1012117).

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