Video transmission over MANETs with enhanced quality of experience

Goudarzi, Pejman; Hosseinpour, Mehdi

doi:10.1109/tce.2010.5681093

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…This is calculated in dB (decibel) and obtained from the original frame and the frame received (as shown in equation 2 and equation 3 below) [22]- [23].…”

Section: B Quality Of Experiencementioning

confidence: 99%

Reliable Energy-Efficient Multilayer Mechanism with Realistic Battery Model and QoE Support in Wireless MANETs

Carvalho

Júnior

Francês

2016

J. Microw. Optoelectron. Electromagn. Appl.

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MANET is an important configuration of network for operations that employ mechanisms to enable dynamic selfconfiguration, self-optimization and self-healing, which is a challenging task for the routing protocols, new multimedia application and the battery energy consumption of wireless and mobile devices. These protocols are challenging because battery problems are more serious in this type of network and the existing protocols are not adapted to current problems, particularly mobile multimedia streaming, that can be found in most Internet flow. This paper proposes a reliable energyefficient multilayer mechanism to address this to make improvements in the main current protocols. The proposal adds decision metrics to an energy-efficient computational intelligence technique based on Fuzzy-System with QoE, mobility and battery guarantees, choosing the best path in an efficient and accuracy way. The proposal has been evaluated in a network simulator.

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“…This is calculated in dB (decibel) and obtained from the original frame and the frame received (as shown in equation 2 and equation 3 below) [22]- [23].…”

Section: B Quality Of Experiencementioning

confidence: 99%

Reliable Energy-Efficient Multilayer Mechanism with Realistic Battery Model and QoE Support in Wireless MANETs

Carvalho

Júnior

Francês

2016

J. Microw. Optoelectron. Electromagn. Appl.

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“…It also optimizes bit rate switching [8]. iProxy is a mobile video-centric proxy cache that offers better hit rates and streaming quality as compared to state- [7,9] Playback smoothness Medium Very low [8, 11-14, 28, 31-34, 36-40] Classical objective quality metric Very low Low [4, 15-21, 41, 117] Subjective quality metric Very low Medium [26][27][28][29][30][31][32][33] Subjective quality metric for scalable video coding Medium Medium [43][44][45][46][47][48][49][50] Subjective QoE metric in lossy environment High High [22,42,[59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] Video quality metric for advanced video coding High High [74][75][76][77][78][79][80][81][82]…”

Section: Playback Smoothness As Quality Metricmentioning

confidence: 99%

“…Currently, different metrics are applied in cross-layer approaches to improve QoE [45][46][47][48][49]. A work in [45] was proposed to aim to maximize the utility function at the application layer.…”

Section: Subjective Quality Mapped From Classical Objective Metricmentioning

confidence: 99%

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QoE in video streaming over wireless networks: perspectives and research challenges

Bai

et al. 2015

Wireless Netw

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The deployment of 3G/LTE networks and advancements in smart mobile devices had led to high demand for multimedia streaming over wireless network. The rapid increasing demand for multimedia content poses challenges for all parties in a multimedia streaming system, namely, content providers, wireless network service providers, and smart device makers. Content providers and mobile network service providers are both striving to improve their streaming services while utilizing advancing technologies. Smart device makers endeavor to improve processing power and displays for better viewing experience. Ultimately, the common goal shared by content providers, network service providers, and smart device manufactures is to improve the QoE for users. QoE is both an objective and a subjective metric measuring the streaming quality experience by end users. It may be measured by streaming bitrate, playback smoothness, video quality metrics like Peak to Signal Noise Ratio, and other user satisfaction factors. There have been efforts made to improve the streaming experiences in all these aspects. In this paper, we conducted a survey on existing literatures on QoE of video streaming to gain a deeper and more complete understanding of QoE quality metrics. The goal is to inspire new research directions in defining better QoE and improving QoE in existing and new streaming services such as adaptive streaming and 3D video streaming.

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“…As we write, several solutions for video streaming over adhoc networks have begun to appear, e.g., based on centralised overlay construction supervised by the video source [24], or on simple loss-distortion models for one-to-one communication [25]. However, our technique still distinguishes itself from the others on some unique features.…”

Section: Introductionmentioning

confidence: 99%

Low-Latency Video Streaming With Congestion Control in Mobile Ad-Hoc Networks

Greco

Cagnazzo

Pesquet‐Popescu

2012

IEEE Trans. Multimedia

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Abstract-In this paper, we address the challenge of delivering a video stream, encoded with multiple descriptions, in a mobile ad-hoc environment with low-latency constraints. This kind of application is meant to provide an efficient and reliable video communication tool in scenarios where the deployment of an infrastructure is not feasible, such as military and disaster relief applications. First, we present a recently proposed protocol that employs a reliable form of one-hop broadcast to build an efficient overlay network according to a multi-objective function that minimises the number of packets injected in the network and to maximises the path diversity among descriptions. Then, we introduce the main contribution of this article: a cross-layer congestion control strategy where the MAC layer is video-coding aware and adjusts its transmission parameters (namely, the RTS retry limit) via congestion/distortion optimisation. The main challenge in this approach is providing a reliable estimation of congestion and distortion, given the limited information available at each node. Our simulations show that, if a stringent constraint of low delay is imposed, our technique grants a consistent gain in terms both of both PSNR and delay reduction, for bitrates up to a few megabits per second.

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Video transmission over MANETs with enhanced quality of experience

Cited by 12 publications

References 16 publications

Reliable Energy-Efficient Multilayer Mechanism with Realistic Battery Model and QoE Support in Wireless MANETs

Reliable Energy-Efficient Multilayer Mechanism with Realistic Battery Model and QoE Support in Wireless MANETs

QoE in video streaming over wireless networks: perspectives and research challenges

Low-Latency Video Streaming With Congestion Control in Mobile Ad-Hoc Networks

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