Title
LightFEC: Network Adaptive FEC with a Lightweight Deep-Learning Approach
Abstract
ABSTRACTNowadays, the interest of real-time video streaming reaches a peak. To deal with the problem of packet loss and optimize users' Quality of Experience (QoE), Forward error correction (FEC) has been studied and applied extensively. The performance of FEC depends on whether the future loss pattern is precisely predicted, while the previous researches have not provided a robust packet loss prediction method. In this work, we propose LightFEC to make accurate and fast prediction of packet loss pattern. By applying long short-term memory (LSTM) networks, clustering algorithms and model compression methods, LightFEC is able to accurately predict packet loss in various network conditions without consuming too much time. According to the results of well-designed experiments, we find out that LightFEC outperforms other schemes on prediction accuracy, which improves the packet recovery ratio while keeping the redundancy ratio at a low level.
Year
DOI
Venue
2021
10.1145/3474085.3475528
International Multimedia Conference
DocType
Citations 
PageRank 
Conference
1
0.36
References 
Authors
0
4
Name
Order
Citations
PageRank
Han Hu110.36
Sheng Cheng210.69
Xinggong Zhang311515.63
Zongming Guo477881.98