Int. J. Communications, Network and System Sciences, 2013, 6, 204-220 http://dx.doi.org/10.4236/ijcns.2013.65024 Published Online May 2013 (http://www.scirp.org/journal/ijcns) Multimedia Streaming for Ad Hoc Wireless Mesh Networks Using Network Coding Basil Saeed 1 , Chung-Horng Lung 1 , Thomas Kunz 1 , Anand Srinivasan 2 1 Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada 2 EION Inc., Ottawa, Canada Email: bsaeed@sce.carleton.ca, chlung@sce.carleton.ca, tkunz@sce.carleton.ca, anand@eion.com Received March 2, 2013; revised April 2, 2013; accepted May 2, 2013 Copyright © 2013 Basil Saeed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT Over the past years, we have witnessed an explosive growth in the use of multimedia applications such as audio and video streaming with mobile and static devices. Multimedia streaming applications need new approaches to multimedia transmissions to meet the growing volume demand and quality expectations of multimedia traffic. This paper studies network coding which is a promising paradigm that has the potential to improve the performance of networks for mul- timedia streaming applications in terms of packet delivery ratio (PDR), latency and jitter. This paper examines several network coding protocols for ad hoc wireless mesh networks and compares their performance on multimedia streaming applications with optimized broadcast protocols, e.g., BCast, Simplified Multicast Forwarding (SMF), and Partial Dominant Pruning (PDP). The results show that the performance increases significantly with the Random Linear Net- work Coding (RLNC) scheme. Keywords: Wireless Broadcast; Multimedia Streaming; Audio Streaming; Video Streaming; Network Coding; Random Linear Network Coding; PDP; SMF; BCast 1. Introduction Broadcasting is a linear transmission mechanism includ- ing audio and video traffic in real time. Several types of devices such as TVs, radios, computers and mobile de- vices (cell phones) are used as receivers to gain access to one broadcasted traffic flow at a time per channel with pre-scheduled start and end times. The receivers are con- trolled by the users to be switched on or off, as well as being controlled by frequency tuning. Practically, all of the content offered by mobile, radio and TV stations to- day are available only in this approach. The digital mul- timedia broadcasting standards support high definition television (HDTV), multiple standard definition televi- sion (SDTV) program streams, and private data applica- tions such as broadcast duplicate transmissions, multi- media pager data burst, HTML pages, audio streaming, video streaming, etc. [1]. With the explosion of Internet traffic seen over the past two decades, coupled with the ever increasing need to access critical data at any location, wireless networks have emerged as a means of effectively communicating in an on-demand fashion from nearly any location. This new communication paradigm is inherently based on a broadcast medium and presents challenges not seen in traditional wire line networks due to the nature of the wireless medium in which users must share access to: 1) frequencies or 2) time-slots controlled by the Medium Access Control (MAC) model used. The challenges in- clude bandwidth limitations, mobility impacts, energy consumption, unreliable transmission, security issues and dead spots. We have witnessed an explosive growth in the use of multimedia applications with mobile and static devices lately. Multimedia broadcasting in wireless networks in- tends to transmit concurrently identical multimedia traf- fic to multiple receivers. The main important trends for mobile and static multimedia broadcasting are: 1) Mobile traffic is growing significantly, and will be dominated by video and voice; 2) Mobile devices are getting more powerful; 3) mobile graphics are getting better [2]. As a result, mobile multimedia users are expected to grow rapidly as shown in Figure 1 which shows the growth trend for audio streaming applications presented in [3], and Figure 2 which shows the growth trend for TV and video user as it is expected to blossom to more than four hundred and fifty million by 2014 [2]. Copyright © 2013 SciRes. IJCNS