论文标题
南希：通过无线网络进行视频分布的神经自适应网络编码方法
NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks
论文作者
论文摘要
本文介绍了Nancy，该系统使用加固学习（RL）在无线网络上生成自适应比特率（ABR），用于视频和自适应网络编码率（ANCR）。南希（Nancy）训练一个神经网络模型，其奖励为经验质量（QOE）指标。它执行关节优化以选择：（i）将来的视频块自适应比特率，以应对可用带宽的变化和（ii）自适应网络编码速率，以编码视频块切片以抵消无线网络中的数据包损失。我们介绍了南希的设计和实施，并与包括Pensieve和RobustMPC在内的最先进的视频速率适应算法相比，评估了其性能。我们的结果表明，南希的平均QoE分别比Pensieve和RobustMPC高29.91％和60.34％。
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.
