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

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%similar to 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.

Automated summary

Fueled by emerging short video applications, streaming short-form videos has become ubiquitous among mobile users. However, frequent video switching results in significant data loss, which is financially burdensome for both users and vendors. To tackle this problem, this study proposes a novel system called DUASVS, which uses integrated learning to capture network conditions and trains intelligent adaptation models to reduce data loss and save data usage.