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Improving quality of experience (QoE) on Internet Video delivery

Recent years have seen a tremendous increase in the popularity of Internet video, which forms a major fraction of Internet traffic today, and Cisco technical report says that Internet video traffic expects to be 30 exabyte in 2020. In this trend, delivering high quality of experience (QoE) is critical since it correlates with user engagement and revenue. The project is investigating to deliver high quality video across diverse and variable network conditions. Most Internet video delivery uses adaptive bitrate (ABR) algorithms, combined with HTTP chunk-based streaming protocols, and ABR algorithm is one of the critical part for success of high QoE Internet video delivery. However, these ABR algorithms today have fixed and closed source implementations which results in two problems: 1) content publishers can't customize them according to their preference 2) no single algorithm works well across the diverse range of bandwidth conditions in the wild. In order to solve these problems we propose a novel video delivery pipeline. Our results show that our approach can improve the median QoE by 37% compared to a commercial ABR.

People:

Collaborators

  • Prof. Ramesh Govindan (University of Southern California)
  • Prof. Ethan Katz-Bassett (University of Southern California)
  • Prof. Jessica Chen (University of Windsor)
  • Hui Zhang (Conviva)
  • Jibin Zhan (Conviva)

Students

  • Yun Seong Nam (Purdue University)
  • Zahaib Akhtar (University of Southern California)

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