Latency Aware Adaptive Video Streaming using Ensemble Deep Reinforcement Learning

The development of live broadcasting represents many new technical challenges on adaptive bitrate(ABR) algorithms, which not only requires stable and high-quality transmission but also low end-to-end latency. Reinforcement learning(RL) achieves promising results and can learn ABR algorithms automatically without using any pre-programmed control rules. However, existing methods only consider bitrate control and ignore latency control. Therefore, in order to effectively reduce the end-to-end latency, we propose an independent latency limit model to control the frame skipping. Moreover, a model ensemble algorithm is implemented to reduce performance variance and improve the user quality of experience (QoE). Experimental results show that our model outperforms base- line methods and demonstrate the effectiveness of our model.