A Deep Reinforcement Learning-Based Optimal Transmission Control Method for Streaming Videos

Time delay and image quality degradation are main challenges faced by streaming video transmission. How to make adaptive planning for transmission schemes according to dynamic change of transmission environment, always remains a technical concern. As a consequence, this paper proposes a deep reinforcement learning-based optimal transmission control method for streaming videos. Firstly, edge buffer task allocation is combined with quality of experience (QoE)-oriented deep reinforcement learning algorithm, in order to develop a resource allocation method for streaming videos. Secondly, an actively coordinated streaming data streaming transmission mechanism is established to construct a specific optimal transmission control method that satisfies environment requirement. Finally, a set of experiments are conducted to verify effectiveness and performance on public video transmission datasets. And the proposal is compared with several traditional transmission methods. The experiments show that the proposal in this work can effectively reduce delay and startup time and improve the QoE. This shows that the proposal is able to bring better stability and transmission quality.