DUASVS: A Mobile Data Saving Strategy in Short-form Video Streaming

Guanghui Zhang, Jie Zhang*, Ke Liu, Jing Guo, Jack Lee, Haibo Hu, Vaneet Aggarwal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Fueled by the emerging short video applications (e.g., TikTok), streaming short-form videos nowadays is ubiquitous among mobile users. During the viewing, one common action is to scroll the screen to switch videos, which is a handy operation for the viewers to quickly search for content of interest. However, our empirical measurements reveal that frequent video switching can result in nearly half of the mobile data quota being used for transferring the video data that is never watched. This problem is called data loss in this work. Given the immense cost of the network infrastructure, such a high proportion of data loss is financially tremendous to both mobile users and streaming vendors. To tackle the problem, this study proposes a novel system called Data Usage Aware Short Video Streaming (DUASVS), where a new Integrated Learning is used to capture the characters of past network conditions and then trains intelligent adaptation models to reduce data loss and save data usage. Extensive evaluations show that DUASVS is able to save 70.7%~83.2% of mobile data usage without incurring any QoE degradation. Moreover, the system exhibits strong robustness, performing consistently over a wide range of network environments as well as video streaming sessions.
Original languageEnglish
JournalIEEE Transactions on Services Computing
DOIs
Publication statusE-pub ahead of print - 10 Feb 2022

Scopus Subject Areas

  • Information Systems and Management
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

User-Defined Keywords

  • Short video streaming
  • Mobile network
  • Data usage
  • Quality-of-Experience
  • Video reliability

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