Digital streaming media distribution and transmission process optimisation based on adaptive recurrent neural network

• Published in: Connection science Vol. 34; no. 1; pp. 1169 - 1180
• Main Author: Shan, Wenjing
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; data scheduling; Digital streaming media; Media; Network latency; Neural networks; Optimization; recurrent neural network; Recurrent neural networks; Rejection rate; resource efficiency; Streaming media; transmission optimisation
• ISSN: 0954-0091; 1360-0494; 1360-0494
• DOI: 10.1080/09540091.2022.2052264

With the rapid growth of streaming media services, users have higher and higher requirements for streaming media transmission rates and network experience. Experiments show that in multi-path streaming media transmission services, supporting streaming services with high bandwidth and low latency is a very challenging task. Based on this, this article explores and establishes a digital streaming media distribution and transmission process optimisation model based on an adaptive recurrent neural network. This paper proposes a priority-aware streaming media multi-path data scheduling mechanism, which allows applications to distinguish the relative importance of data and ensure that high-priority data is transmitted on the path with the best quality. The adaptive recurrent neural network algorithm is used in the optimisation process of the distribution and transmission process. By simulating the real environment, it is verified that the model can improve the efficiency of distribution resources and reduce the access rejection rate and data jitter caused by interruption.