基于GA-BP的实时视频通信自适应前向纠错码

针对在动态的网络环境中，实时视频在基于数据报协议的传输中遇到大量丢包的问题，提出了一种基于遗传算法改进的BP神经网络自适应前向纠错码的方法。该方法通过设计自适应的前向纠错编码来恢复丢失的数据包。首先，设计一个基于帧级别的前向纠错的框架，主要包括视频编码解码器、RS前向纠错码编解码模块和前向纠错码冗余度计算模块，用来模拟一般视频传输的环境；然后，设计GA-BP模型，将其应用到RS-FEC的冗余度计算，实现一种帧级不均等的保护方案；最后，基于GE信道模拟网络，生成丢包数据，训练离线模型，进行实验验证。实验结果表明，相比StaticRS和DeepRS，所提方法能够在较低冗余度下较高地恢复丢失的数据包，得到更高质量的视频。

Research on adaptive forward error correction codes for real time video communication based on GA-BP*

In the dynamic network environment, real-time video transmission based on datagram protocols will encounter a large number of packet loss issues. This paper proposed an adaptive forward error correction code method based on GA-BP neural network. This method recovered lost data packets by designing adaptive forward error correction codes. Firstly, the paper designed a frame level forward error correction framework, which mainly included video codec, RS forward error correction codec module, and forward error correction code redundancy calculation module, to simulate the general video transmission environment. Then, the paper designed the GA-BP model and applied it to the redundancy calculation of RS-FEC to achieve a frame level unequal protection scheme. Finally, based on GE channel simulation network, it generated packet loss data, trained offline model, and conducted experimental verification. The experiments results show that compared to StaticRS and DeepRS, this method can recover lost data packets more efficiently with lower redundancy, resulting in higher quality video.