Optimal Network Coding based on Machine Learning Methods for Collaborative Networks

Mendoza-Almanza, Javier; López-Fuentes, Francisco de Asís

doi:10.1109/codit.2019.8820477

Cited by 4 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the recent proposals on using machine learning along with network coding include [19], [20], [21] and [22]. The application of machine learning on physical layer network coding on signals is proposed in [19] with the aim on achieving higher throughput, while [20] investigates the use of ML on designing the coordinator server to synchronize all nodes, and to assign their roles during a NC operation. Further, [21] proposed the role of ML for construction of network codes and [22] investigates the integration of ML with NC for wireless broadcast.…”

Section: Related Workmentioning

confidence: 99%

Nancy: Neural Adaptive Network Coding Methodology for Video Distribution Over Wireless Networks

Saxena

Naresh

Gupta

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

View full text Add to dashboard Cite

This paper presents NANCY, a system that generates adaptive bit rates (ABR) for video and adaptive network coding rates (ANCR) using reinforcement learning (RL) for video distribution over wireless networks. NANCY trains a neural network model with rewards formulated as quality of experience (QoE) metrics. It performs joint optimization in order to select: (i) adaptive bit rates for future video chunks to counter variations in available bandwidth and (ii) adaptive network coding rates to encode the video chunk slices to counter packet losses in wireless networks. We present the design and implementation of NANCY, and evaluate its performance compared to state-of-the-art video rate adaptation algorithms including Pensieve and robustMPC. Our results show that NANCY provides 29.91% and 60.34% higher average QoE than Pensieve and robustMPC, respectively.

show abstract