Alfie: Neural-Reinforced Adaptive Prefetching for Short Videos

Short videos have received extraordinary success in recent years. To provide smooth playback and avoid rebuffering delay, prefetching upcoming videos is commonly used in cellular networks. Current prefetching designs fall short in dealing with bandwidth overhead, especially the exit overhead of downloaded but unconsumed chunks due to user exit. Measurement from a large short video platform shows that exit overhead accounts for up to 43.5% of bandwidth overhead. Thus we build Alfie, a bandwidth-efficient short video prefetching algorithm via reinforcement learning. Essentially Alfie adjusts prefetching based upon user viewing patterns in addition to network conditions. We demonstrate that Alfie outperforms the state of the art by up to 26.8% in overall performance while reducing the exit overhead by up to 84.9%.