NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks
This addresses video streaming quality issues for users on wireless networks, representing an incremental improvement over existing methods.
The paper tackled video distribution over wireless networks by proposing NANCY, a system that uses reinforcement learning to jointly optimize adaptive bit rates and network coding rates, resulting in 29.91% and 60.34% higher average quality of experience compared to state-of-the-art methods like Pensieve and robustMPC.
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.