Multipath Smart Preloading Algorithms in Short Video Peer-to-Peer CDN Transmission Architecture

The rapid growth of short video users has brought high traffic costs to content providers. Saving Content Distribution Network (CDN) costs while maintaining users’ playback Quality of Experience (QoE) is a significant problem for major short video platforms like Douyin (the Chinese counterpart of TikTok). Two commonly utilized cost-saving methods: PCDN (peer-to-peer CDN, fetching data from multiple low-cost but low-bandwidth edge devices with a multipath transmission protocol) and preloading control (minimizing unnecessary future playing data by downloading videos in segmented data ranges), each may hurt users’ experience respectively. Worse still, both methods combined have a synergistic negative effect over QoE. In this paper, we focus on experiences, algorithms, and prospects to solve this cost-QoE dilemma. We first introduce Douyin’s current PCDN multipath architecture and then review learning-based preloading techniques. Second, based on Reinforcement Learning (RL), we propose a Multipath-aware Smart Preloading algorithm, which consists of three schemes: one to decide the best size of the next range of preloaded data, another to design a water level valve algorithm that prioritizes preloading between currently playing video’s unfinished data and the next video’s beginning data, and the last one to determine the bitrate level of the next video. Douyin’s anonymous user feedback shows our Smart Preloading algorithm reduces traffic waste by $\sim 26$ % while ensuring QoE. Third, we analyze and outlook the future of video systems, including trends in PCDN and other open issues.