  In the realm of short video streaming, popular adaptive bitrate (ABR)
algorithms developed for classical long video applications suffer from
catastrophic failures because they are tuned to solely adapt bitrates. Instead,
short video adaptive bitrate (SABR) algorithms have to properly determine which
video at which bitrate level together for content prefetching, without
sacrificing the users' quality of experience (QoE) and yielding noticeable
bandwidth wastage jointly. Unfortunately, existing SABR methods are inevitably
entangled with slow convergence and poor generalization. Thus, in this paper,
we propose Incendio, a novel SABR framework that applies Multi-Agent
Reinforcement Learning (MARL) with Expert Guidance to separate the decision of
video ID and video bitrate in respective buffer management and bitrate
adaptation agents to maximize the system-level utilized score modeled as a
compound function of QoE and bandwidth wastage metrics. To train Incendio, it
is first initialized by imitating the hand-crafted expert rules and then
fine-tuned through the use of MARL. Results from extensive experiments indicate
that Incendio outperforms the current state-of-the-art SABR algorithm with a
53.2% improvement measured by the utility score while maintaining low training
complexity and inference time.
