Pseudo-Systematic Decoding of Hybrid Instantly Decodable Network Code for Wireless Broadcasting

Wang, Jian; Xu, Kui; Xu, Youyun; Zhang, Dongmei

doi:10.1109/lwc.2018.2830361

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Because of these properties, IDNCs have been the subject of several work studying real time multimedia broadcast [9], [10], [24]. Some existing works such as [25] and [26] relax the second property, and allow a receiver to temporary store non-instantly decodable packets (instead of discarding them) and utilize them later in the decoding process. This approach allows the throughput performance gap between IDNC and RLNC to be reduced.…”

Section: Related Workmentioning

confidence: 99%

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Arefi

Khabbazian

2021

IEEE Open J. Comput. Soc.

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Packet loss recovery in wireless broadcast is challenging, particularly for real-time applications which have strict and short delivery deadline. To recover the maximum number of lost packets within a short time, existing packet recovery solutions often rely on instantly decodable network coding (IDNC). Some of these solutions can recover nearly the maximum number of lost packets possible at the cost of collecting feedback from all (or a large percentage of) users. This is impractical in dense networks. In addition, their runtime grows with the number of users, which is not desirable due to the urgent delivery deadline of real-time applications. In this work, we introduce random instantly decodable network coding (RIDNC), a random encoding approach to IDNC. We propose RAndom IDNC Encoder (RACE), a fast RIDNC encoder that can recover nearly as many lost packets as the optimal RIDNC encoder. We compare RACE with the CrowdWiFi encoder, a high performing packet loss recovery solution used in CrowdWiFi, a commercial system for broadcasting live video in dense networks. We show that RACE is up to two orders of magnitude faster than the CrowdWiFi encoder, and recovers more lost packets in practice, where there is not enough time to collect feedback from many users.

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Section: Related Workmentioning

confidence: 99%

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Arefi

Khabbazian

2021

IEEE Open J. Comput. Soc.

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“…Aiming at this problem, Duffield et al proposed a novel unicast probe packet method called packet stripes, which improves the accuracy of loss rate estimation by enhancing the correlation of the packets within packet stripes. Recently, Sattari et al developed a novel framework for link loss rates inference in the network with the capability of network coding and this technology can also be extended to wireless sensor networks; however, the restriction of this technology is that the network coding technology is not widely used in actual networks.…”

Section: Related Workmentioning

confidence: 99%

Time‐varying network link loss rate tracking from unicast end‐to‐end measurements

Fei

2019

Int J Communication

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Time-varying network link loss rate is a useful information for network managers to discover and locate the network link failures. This paper proposes a method to track time-varying network link loss rates from unicast end-to-end measurements. The method first trains a state transition matrix to capture the spatio-temporal characters of packet link transmission probabilities by sending end-to-end probe packets and then estimates the time-varying link loss rates using the state transition matrix and the end-to-end measurements obtained from background traffic (the existed packets in network). We also introduce a validation step to check and retrain the state transition matrix in order to ensure the accuracy of the state transition matrix. Our method is capable of tracking the variation of link loss rates without incessantly sending probe packets, which is very feasible for many practical applications. The ns-2 simulation results show the good performance of our method.KEYWORDS end-to-end measurement, link loss rate, network tomography, time-varying INTRODUCTIONWith the rapid development of the network technology, the scale and the complexity of the Internet are increasing sharply. In order to efficiently design, control, and optimize the network, it is essential to timely understand the accurate network internal performance parameters, such as link loss rate and link delay. Traditional methods obtain the network internal performance parameters by measuring them directly on the basis of the cooperation of internal nodes. However, these methods fail if the cooperation of the internal nodes is infeasible because it may incur security problem to the network.Network tomography 1,2 is regarded as a promising technique in network measurement field, because it can infer the network internal performance parameters (such as link delay, 3,4 link loss, 5-10 link bandwidth, 11 and neutral link, 12 etc) without deploying monitors inside the network. This technology can also be extended to infer network topology 13,14 and traffic matrix. 15,16 Since it is more practical and scalable than the internal cooperation-based methods, network tomography has attracted many studies since it was proposed.Network link loss inference (also called loss tomography) is an important component of network tomography, and numerous ways have also been developed to infer link loss from end-to-end measurements. However, most of these methods are under the assumption that the link-states are stationary, and the loss rates remain unchanged in the measurement period. The link loss rates are estimated by sending amount of probe packets from a source node to a set of destination nodes.In actual, the bursty property of network traffic makes the link-states change frequently, and the link loss rates can also be varied with the changing of the link-states. Hence, it is necessary to send the probe packets incessantly in order to Int J Commun Syst. 2019;32:e4070.wileyonlinelibrary.com/journal/dac

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“…In hu et al, 20 a new opportunistic network coding scheme is introduced for a multirelay‐aided single‐source single‐destination network with transmission deadline. The encoding and decoding of instantly decodable network code are extended and also proposed a hybrid instantly decodable network code in Wang et al 21 In Hu et al, 22 an adaptive random network coding multicasting of hard deadline constrained prioritized data is investigated for cooperative mobile devices with dual interfaces. Ning et al 23 suggest an intent‐based traffic control system by employing deep reinforcement learning for 5G‐envisioned internet of connected vehicles (IoCVs).…”

Section: Introductionmentioning

confidence: 99%

Performance of new relay selection scheme for buffer‐assisted multihop decode‐and‐forward networks

Bhatnagar

Dixit²,

Gauttam

et al. 2021

Int J Communication

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Summary In this paper, a new relay selection scheme is proposed to reduce the end‐to‐end packet delivery delay for buffer‐assisted multihop decode‐and‐forward cooperative networks. The proposed method selects a relay node having more packets in the associated buffer and relay's proximity to the destination node. Mathematical expressions for the outage probability and average packet delay in Rician fading are obtained by modeling the system as a Markov chain. The proposed relay selection scheme has less packet delay as compared to the max‐link relay selection scheme with marginally higher outage probability. Thus, the proposed relay selection scheme is a good alternative to low latency wireless applications.

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Pseudo-Systematic Decoding of Hybrid Instantly Decodable Network Code for Wireless Broadcasting

Cited by 9 publications

References 8 publications

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Time‐varying network link loss rate tracking from unicast end‐to‐end measurements

Performance of new relay selection scheme for buffer‐assisted multihop decode‐and‐forward networks

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