NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks

TL;DR

NANCY is a neural network-based system that optimizes video bit rates and network coding rates using reinforcement learning to improve video streaming quality over wireless networks.

Contribution

It introduces a joint optimization approach with reinforcement learning for adaptive bit rate and network coding rate selection in wireless video distribution.

Findings

NANCY achieves approximately 30% higher QoE than Pensieve.

NANCY outperforms robustMPC with over 60% higher QoE.

The system effectively adapts to bandwidth variations and packet losses.

Abstract

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.