Packet-Preserving Network Coding Schemes for Padding Overhead Reduction

Schütz, Bertram; Aschenbruck, Nils

doi:10.1109/lcn44214.2019.8990879

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…To realize the packet loss and to improve video streaming, a simple architecture is developed with 3 modules 1) Transmitter 2) A packet failure model 3) A Receiver.The architecture consists of compressed video data using modified H.265 protocol [14]. The compressed video data is transmitted using 802.11p protocol.…”

Section: Methodsmentioning

confidence: 99%

Optimized Visual Internet of Things in Video Processing for Video Streaming

Shilpa¹,

Kumar²,

Rao³

2023

IJRITCC

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The global expansion of the Visual Internet of Things (VIoT) has enabled various new applications during the last decade through the interconnection of a wide range of devices and sensors.Frame freezing and buffering are the major artefacts in broad area of multimedia networking applications occurring due to significant packet loss and network congestion. Numerous studies have been carried out in order to understand the impact of packet loss on QoE for a wide range of applications. This paper improves the video streaming quality by using the proposed framework Lossy Video Transmission (LVT) for simulating the effect of network congestion on the performance of encrypted static images sent over wireless sensor networks.The simulations are intended for analysing video quality and determining packet drop resilience during video conversations.The assessment of emerging trends in quality measurement, including picture preference, visual attention, and audio visual quality is checked. To appropriately quantify the video quality loss caused by the encoding system, various encoders compress video sequences at various data rates.Simulation results for different QoE metrics with respect to user developed videos have been demonstrated which outperforms the existing metrics.

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Section: Methodsmentioning

confidence: 99%

Optimized Visual Internet of Things in Video Processing for Video Streaming

Shilpa¹,

Kumar²,

Rao³

2023

IJRITCC

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“…With increasing D , the double exponentially increasing packet length M may cause the practical issue of an excessive padding overhead. Such an issue can be effectively solved based on the methods proposed in [ 16 , 17 ].…”

Section: Framework Descriptionmentioning

confidence: 99%

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Tang

Zheng

et al. 2022

Entropy

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In complex network environments, there always exist heterogeneous devices with different computational powers. In this work, we propose a novel scalable random linear network coding (RLNC) framework based on embedded fields, so as to endow heterogeneous receivers with different decoding capabilities. In this framework, the source linearly combines the original packets over embedded fields based on a precoding matrix and then encodes the precoded packets over GF(2) before transmission to the network. After justifying the arithmetic compatibility over different finite fields in the encoding process, we derive a sufficient and necessary condition for decodability over different fields. Moreover, we theoretically study the construction of an optimal precoding matrix in terms of decodability. The numerical analysis in classical wireless broadcast networks illustrates that the proposed scalable RLNC not only guarantees a better decoding compatibility over different fields compared with classical RLNC over a single field, but also outperforms Fulcrum RLNC in terms of a better decoding performance over GF(2). Moreover, we take the sparsity of the received binary coding vector into consideration, and demonstrate that for a large enough batch size, this sparsity does not affect the completion delay performance much in a wireless broadcast network.

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“…This section first gives a short introduction to network coding, following the notation from [32] and [29]. Afterwards, previous works that incorporate network coding into TCP are reviewed.…”

Section: Background and Related Workmentioning

confidence: 99%

“…We propose a network coding framework called PyNC [29] in Python 2.7 that builds upon sagemath [36] 1 and NetfilterQueue [16]. In order to be able to use existing applications on COTS devices and, thus, increase deployability, PyNC provides a shim layer between the application and transport layer.…”

Section: Tcpyncmentioning

confidence: 99%

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Less Is More: Choosing Fair Network Coding Parameters

Thieme

Schütz

Aschenbruck

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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TCP still is the prevailing transport protocol on the Internet. It does, however, suffer from severe performance issues on links with non-congestion losses. Instead of relying on the RTT-sensitive ARQ mechanism of TCP, network coding (NC) uses linear combinations of packets to correct forward erasures. Several approaches were developed to incorporate NC into TCP. While they are able to improve the offered service, their lack of deployability and fairness in modern environments is not desirable. To mitigate this, we propose TCPyNC, a Python-based intra-session NC user space shim layer for TCP. Not only are its gains equally high, but it is also more flexible regarding the use of TCP options and parameters. We use a numerical analysis and a testbed evaluation to determine parameters that enable a fair use of TCP in conjunction with Forward Erasure Correction (FEC). In order to reach this goal, we found the maxim less is more to be a sensible orientation. For scenarios with 10% random loss, instead of choosing a high redundancy rate and a large generation size in order to gain the best loss reduction, choosing redundancy rates that leave between 1% and 6% loss for TCP's ARQ to correct and generation sizes below 32 when using default TCP parameters, yields fair results.

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Packet-Preserving Network Coding Schemes for Padding Overhead Reduction

Cited by 13 publications

References 11 publications

Optimized Visual Internet of Things in Video Processing for Video Streaming

Optimized Visual Internet of Things in Video Processing for Video Streaming

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Less Is More: Choosing Fair Network Coding Parameters

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