WiFi multicast streaming using AL-FEC inside the trains of high-speed rails

Chiao, Hsin-Ta; Chang, Shih-Ying; Li, Kuan‐Ming; Kuo, Yi-Ting; Tseng, Ming-Chien

doi:10.1109/bmsb.2012.6264293

Cited by 29 publications

(15 citation statements)

References 15 publications

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“…Many of the schemes to improve multicast services are based on integrating Automatic Repeat Request (ARQ) mechanisms into the protocol architecture [14], [15], [17]- [19], adding Forward Error Correction (FEC) packets to the multicast stream [20], [21], or both [22]. Other studies propose rate adaptation mechanisms for improved network utilization [23].…”

Section: Related Workmentioning

confidence: 99%

Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation

Gupta

Bejerano

Gutterman

et al. 2016

IEEE/ACM Trans. Networking

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WiFi networks have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this paper, we present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe is based on accurate receiver feedback and incurs a small control overhead. In particular, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information about the channel quality to the multicast sender. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. AMuSe does not require any changes to the standards or any modifications to the WiFi devices. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices, both with and without interference sources. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions.

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Section: Related Workmentioning

confidence: 99%

Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation

Gupta

Bejerano

Gutterman

et al. 2016

IEEE/ACM Trans. Networking

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show abstract

“…Nearly all of the solutions to improve multicast services rely either on integrating automatic repeat request (ARQ) mechanisms into the protocol architecture [10]- [14], or adding FEC to the multicast stream [15], [16], or both [17]. Other studies propose rate adaptation mechanisms for improving network utilization [18].…”

Section: Related Work On Wireless Multicastmentioning

confidence: 99%

Experimental Evaluation of a Scalable WiFi Multicast Scheme in the ORBIT Testbed

Bejerano¹,

Ferragut

Guo³

et al. 2014

2014 Third GENI Research and Educational Experiment Workshop

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IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of the mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In earlier work, we proposed a dynamic scheme called AMuSe that selects a subset of the multicast receivers as feedback nodes. The feedback nodes periodically send information about channel quality to the multicast sender and the sender in turn can optimize multicast service quality, e.g., by dynamically adjusting transmission bit-rate. In this paper, we discuss several experimental results for the performance evaluation of AMuSe. Our experiments use more than 250 nodes placed in a grid topology in the ORBIT testbed. We consider different experimental scenarios: with and without the presence of external noise. Our focus is on studying the performance of WiFi nodes in WiFi multicast and establishing the conditions that make AMuSe an attractive scheme for feedback in WiFi multicast.

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“…1. To solve this problem, many studies have investigated AL-FEC in many communication systems [8,[14][15][16]. AL-FEC covers the packet loss not recovered by channel coding because it is applied above layer 2 and uses the fountain code known as the rateless erasure code.…”

Section: Benefit Of Al-fecmentioning

confidence: 99%

Transfer time analysis of file transfer framework with AL-FEC in SOTM networks

Lee

Kim

2015

J Wireless Com Network

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In this paper, we propose a fully reliable file-transfer framework with application layer forward error correction (AL-FEC) in satellite communications on the move (SOTM) systems. In particular, the proposed framework uses a two-way acknowledgement (ACK) exchange mechanism. The proposed framework is implemented into a performance-enhancing proxy to minimize the system change. Furthermore, we analyze the file-transfer time of the proposed framework by Markov chain. The analysis results show that the proposed framework outperforms TCP in terms of the file-transfer time and the goodput. Furthermore, in the transfer of a large-sized file, the overhead of the proposed AL-FEC mechanism is small in terms of the resource efficiency.

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WiFi multicast streaming using AL-FEC inside the trains of high-speed rails

Cited by 29 publications

References 15 publications

Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation

Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation

Experimental Evaluation of a Scalable WiFi Multicast Scheme in the ORBIT Testbed

Transfer time analysis of file transfer framework with AL-FEC in SOTM networks

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