MOS-Based Rate Adaption for VoIP Sources

Moura, Nilmax Teones; Vianna, B. A.; Albuquerque, Célio; Rebello, Vinod E. F.; Boeres, Cristina

doi:10.1109/icc.2007.108

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…[19] proposes a method for QoS control where QoE feedback is used to vary the codec's bitrate in a voice call. This work was further improved by [13]. Here the QoE information is used by end-systems and not by the network.…”

Section: Related Workmentioning

confidence: 97%

ConEx based QoE feedback to enhance QoE

Shirazipour

Charlot

Lefebvre

et al. 2012

Proceedings of the 2012 ACM Workshop on Capacity Sharing

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Quality of service (QoS) generally represents the performance of packet networks. Quality of experience (QoE) defines the quality perceived by end-users of applications running on these networks. This paper relates these two metrics in a novel way using the newly defined congestion exposure (ConEx) mechanism. ConEx is an experimental protocol defined by the IETF that allows the sender of a flow to convey the received explicit congestion notification (ECN) information back into the network. In IPv6, ConEx is implemented in an option header with 28 unused bits. These bits can be used to convey more than ECN feedback towards the network. This paper proposes to use these bits to send real-time objective QoE information, as perceived by the end-users, into the network. Routers can leverage this information to adjust QoS mechanisms. As an example, a new queue management technique is proposed with a multi-field DiffServ classifier using the QoE metric. Simulation results show that this mechanism can help in improving the overall QoE of active flows.

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Section: Related Workmentioning

confidence: 97%

ConEx based QoE feedback to enhance QoE

Shirazipour

Charlot

Lefebvre

et al. 2012

Proceedings of the 2012 ACM Workshop on Capacity Sharing

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“…Frequent changes in adjustable parameters may cause instability on the VoIP system and degrade speech quality even worse. To avoid this, some works employ the additive increase multiplicative decrease (AIMD) algorithm, commonly used for congestion control in TCP [Beritelli et al 2003;Moura et al 2007;Qiao et al 2004]. However, Ngamwongwattana [2007] argues that "the main objective of adaptive-rate control can be different from AIMD because VoIP performance refers to end-to-end quality rather than throughput.…”

Section: Time Dimensionmentioning

confidence: 97%

“…Instead, a few studies have been concentrated on its FEC mechanism, as will be seen in Section 4.2. Barberis et al [2001], Huang et al [2007], and Moura et al [2007] used a hypothetical VBR codec in their simulated mechanisms for adaptive rate control, which consisted of a collection of CBR sources with bitrate values ranging from 8 to 64 kb/s in steps of 8 kb/s. Finally, Abreu-Sernandez and Garcia-Mateo [2000] proposed, to the best of our knowledge, the only study that uses VBR control in an actual VoIP system, implemented on the Motorola DSP96002, with bitrates of 9.4, 7.7, and 5.9 kb/s.…”

Section: Vbr Codecsmentioning

confidence: 99%

Survey on application-layer mechanisms for speech quality adaptation in VoIP

Carvalho

Mota

2013

ACM Comput. Surv.

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VoIP calls are sensitive to several impairments, such as delay and packet loss. One way to overcome these problems is by adaptively adjusting application-layer parameters to keep a minimum speech quality level. At the heart of self-adaptive systems lies a feedback loop, which consists of four key activities: monitoring, analysis, planning, and execution. Nevertheless, the existing adaptive approaches to QoS control of VoIP do not explicitly exhibit this feedback loop. Bringing it to surface can help developers in designing more robust and human-independent VoIP systems. This survey presents a comprehensive review of the current state-of-the-art research on speech quality adaptation of VoIP systems at the application layer and some research challenges on this subject.

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“…The scheme has a scalability shortcoming because the measurement of QoS in a large infrastructure may not be practically feasible. New hybrid adaptive algorithms that adjust the transmission rate of VoIP source according to both network parameters and perceptual quality such as in [13] have been proposed, and shown to result in better speech quality while improving bandwidth utilisation.…”

Section: Related Workmentioning

confidence: 99%

Quality of experience (QoE) driven adaptation scheme for voice/video over IP

et al. 2010

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Network quality of service (NQoS) of IP networks is unpredictable and impacts the quality of networked multimedia services. Adaptive voice and video schemes are therefore vital for the provision of voice over IP (VoIP) services for optimised quality of experience (QoE). Traditional adaptation schemes based on NQoS do not take perceived quality into consideration even though the user is the best judge of quality. Additionally, uncertainties inherent in NQoS parameter measurements make the design of adaptation schemes difficult and their performance suboptimal. This paper presents a QoE-driven adaptation scheme for voice and video over IP to solve the optimisation problem to provide optimal QoE for networked voice and video applications. The adaptive VoIP architecture was implemented and tested both in NS2 and in an Open IMS Core network to allow extensive simulation and test-bed evaluation. Results show that the scheme was optimally responsive to available network bandwidth and congestion for both voice and video and optimised delivered QoE for different network conditions, and is friendly to TCP traffic.

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MOS-Based Rate Adaption for VoIP Sources

Cited by 13 publications

References 23 publications

ConEx based QoE feedback to enhance QoE

ConEx based QoE feedback to enhance QoE

Survey on application-layer mechanisms for speech quality adaptation in VoIP

Quality of experience (QoE) driven adaptation scheme for voice/video over IP

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