Qabr: A qoe-based approach to adaptive bitrate selection in video streaming services

Chanh Minh Tran, Tho Nguyen Duc, Phan Xuan Tan, Eiji Kamioka

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


HTTP Adaptive Streaming (HAS) has recently become the de facto choice of today’s streaming providers to perform a smooth video content delivery to the end users. The key technology behind HAS is the adaptive bitrate selection (ABR) algorithm that adaptively selects the best suitable video bitrate based on either throughput or buffer monitoring techniques. In order to fulfill user’s satisfaction, ABRs must be designed to accurately reflect the perceived quality of experience (QoE), which is influenced by the perceptual and technical factors. However, both throughput and buffer only account for the technical factors, leading to the insufficiency of today’s ABRs in demonstrating human perception. Moreover, existing throughput and buffer-based algorithms are slow-responsive to significant network changes and unstable in terms of video quality, as found by recent research efforts. For those reasons, QABR – a novel QoE-based bitrate selection algorithm – is proposed in this paper that combines the underlying network parameters and user’s instantaneous QoE (in accordance with perceptual factors). Experimental results demonstrate that QABR outperforms the referenced baseline algorithm in various evaluation criteria.

Original languageEnglish
Pages (from-to)138-144
Number of pages7
JournalInternational Journal of Advanced Trends in Computer Science and Engineering
Issue number1.4 S1
Publication statusPublished - 2019


  • Adaptive bitrate selection
  • Buffer
  • HTTP adaptive streaming
  • Quality of experience
  • Throughput

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering


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