Title

Improving Quality Of Voip Streams Over Wimax

Keywords

Aggregation; ARQ; FEC; Fragmentation; R-score; VoIP; WiMax

Abstract

Real-time services such as VoIP are becoming popular and are major revenue earners for network service providers. These services are no longer confined to the wired domain and are being extended over wireless networks. Although some of the existing wireless technologies can support some low-bandwidth applications, the band width demands of many multimedia applications exceed the capacity of these technologies. The IEEE 802.16-based WiMax promises to be one of the wireless access technologies capable of supporting very high bandwidth applications. In this paper, we exploit the rich set of flexible features offered at the medium access control (MAC) layer of WiMax for the construction and transmission of MAC protocol data units (MPDUs) for supporting multiple VoIP streams. We study the quality of VoIP calls, usually given by R-score, with respect to the delay and loss of packets. We observe that loss is more sensitive than delay; hence, we compromise the delay performance within acceptable limits in order to achieve a lower packet loss rate. Through a combination of techniques like forward error correction, automatic repeat request, MPDU aggregation, and minislot allocation, we strike a balance between the desired delay and loss. Simulation experiments are conducted to test the performance of the proposed mechanisms. We assume a three-state Markovian channel model and study the performance with and without retransmissions. We show that the feedback-based technique coupled with retransmissions, aggregation, and variable length MPDUs are effective and increase the R-score and mean opinion score by about 40 percent. © 2008 IEEE.

Publication Date

2-1-2008

Publication Title

IEEE Transactions on Computers

Volume

57

Issue

2

Number of Pages

145-156

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TC.2007.70804

Socpus ID

38349036476 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/38349036476

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