GAZELLE: An Enhanced Random Network Coding Based Framework for Efficient P2P Live Video Streaming Over Hybrid WMNs

Abstract

Although Peer-to-Peer live video streaming over wireless mesh networks (WMNs) is considered a promising technology, some important challenges such as interference, mobility and limited available resources in gadgets (e.g. Smartphones and Tablets) may significantly reduce the perceived video quality. GREENIE and MATIN, in our previous studies, provided an efficient routing protocol in WMNs and a video streaming method based on random network coding (RNC), respectively. Therefore, their integration in the form of an enhanced framework, named GAZELLE, can considerably increase the video quality on these gadgets by decreasing the video distortion, dependency distortion, initial start-up delay and end-to-end delay. Findings using a precise simulation in OMNET++ show that GAZELLE noticeably outperforms other frameworks. GAZELLE not only decreases the imposed computational complexity and transmission overhead due to using RNC considerably, but it also efficiently routes video packets through those gadgets which does not require neither high battery energy sources nor high CPU power.