Expanding-Window BATS Code for Scalable Video Multicasting Over Erasure Networks

Xu, Xiaoli; Zeng, Yong; Guan, Yong Liang; Yuan, Lei

doi:10.1109/tmm.2017.2742699

Cited by 21 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed idea allocated optimal encoding rates to different layers of a video segment to packetize the segment into multiple descriptions with embedded forward error correction for improving quality of experience (QoE) of users. A expandingwindow batched sparse code was proposed in [19] for scalable video multicasting over erasure networks with heterogeneous video quality requirements. In this scheme, the input symbols are grouped into overlapped windows according to their importance levels so that the more important symbols are encoded with lower rate to be decoded by more destinations.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Image Transmission with CoAP for Extreme Environments

Takeshita¹,

Sakaguchi²,

Hisano³

et al. 2022

Preprint

View full text Add to dashboard Cite

Communication in extreme environments is an important research topic for various use cases including environmental monitoring. A typical example is underwater acoustic communication for 6G mobile networks. The major challenges in such environments are extremely high-latency and high-error rate. They make real-time image transmission difficult using existing communication protocols. This is partly because frequent retransmission in noisy networks increases latency and leads to serious deterioration of real-timeness. To address this problem, this paper proposes a stochastic image transmission with Constrained Application Protocol (CoAP) for extreme environments. The goal of the proposed idea is to achieve approximate real-time image transmission without retransmission using CoAP over UDP. To this end, an image is divided into blocks, and value is assigned for each block based on the requirement. By the stochastic transmission of blocks, the reception probability is guaranteed without retransmission even when packets are lost in networks. We implemented the proposed scheme using Raspberry Pi 4 to demonstrate the feasibility. The performance of the proposed image transmission was confirmed from the experimental results.

show abstract

Section: Related Workmentioning

confidence: 99%

Stochastic Image Transmission with CoAP for Extreme Environments

Takeshita¹,

Sakaguchi²,

Hisano³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…When K is small, a better distribution can be obtained via the approach in References [ 65 , 66 , 67 ]. There are also some variants on the degree distribution optimization problem which apply a sliding window [ 68 ] or an expanding window [ 69 ]. When the distribution is optimized, we can guarantee that a given fraction of the input packets can be recovered with high probability.…”

Section: Bats Codesmentioning

confidence: 99%

A Protocol Design Paradigm for Batched Sparse Codes

Yin

Yeung

Yang

2020

Entropy

View full text Add to dashboard Cite

Internet of Things (IoT) connects billions of everyday objects to the Internet. The mobility of devices can be facilitated by means of employing multiple wireless links. However, packet loss is a common phenomenon in wireless communications, where the traditional forwarding strategy undergoes severe performance issues in a multi-hop wireless network. One solution is to apply batched sparse (BATS) codes. A fundamental difference from the traditional strategy is that BATS codes require the intermediate network nodes to perform recoding, which generates recoded packets by network coding operations. Literature showed that advanced recoding schemes and burst packet loss can enhance and diminish the performance of BATS codes respectively. However, the existing protocols for BATS codes cannot handle both of them at the same time. In this paper, we propose a paradigm of protocol design for BATS codes. Our design can be applied in different layers of the network stack and it is compatible to the existing network infrastructures. The modular nature of the protocol can support different recoding techniques and different ways to handle burst packet loss. We also give some examples to demonstrate how to use the protocol.

show abstract

“…For every batch, the encoder first samples a predefined degree distribution to obtain a degree. There are different ways to formulate the degree distribution in different scenarios [37]- [40]. The degree is the number of packets we choose randomly from the input packets to form the batch.…”

Section: A Batched Network Codingmentioning

confidence: 99%

“…To generate a batch, the encoder samples a predefined degree distribution to obtain a degree, where the degree is the number of input packets contributed to the batch. Depends on the application, there are various ways to formulate the degree distribution [20]- [23]. According to the degree, a set of packets is chosen randomly from the input packets.…”

Section: A Batched Network Codingmentioning

confidence: 99%

A Unified Adaptive Recoding Framework for Batched Network Coding

Yin

Tang

et al. 2019

2019 IEEE International Symposium on Information Theory (ISIT)

View full text Add to dashboard Cite

Batched network coding is a variation of random linear network coding which has low computational and storage costs. In order to adapt random fluctuations in the number of erasures in individual batches, it is not optimal to recode and transmit the same number of packets for all batches. Different distributed optimization models, which are called adaptive recoding schemes, were formulated for this purpose.The key component of these optimization problems is the expected value of the rank distribution of a batch at the next network node, which is also known as the expected rank. In this paper, we put forth a unified adaptive recoding framework. We show that the expected rank functions are concave when the packet loss pattern is a stationary stochastic process regardless of the field size, which covers but not limited to independent packet loss and burst packet loss. Under this concavity assumption, we show that there always exists a solution which not only can minimize the randomness on the number of recoded packets but also can tolerate rank distribution errors due to inaccurate measurements or limited precision of the machine. To obtain such an optimal solution, we propose tuning schemes that can turn any feasible solution into a desired optimal solution.

show abstract

Expanding-Window BATS Code for Scalable Video Multicasting Over Erasure Networks

Cited by 21 publications

References 21 publications

Stochastic Image Transmission with CoAP for Extreme Environments

Stochastic Image Transmission with CoAP for Extreme Environments

A Protocol Design Paradigm for Batched Sparse Codes

A Unified Adaptive Recoding Framework for Batched Network Coding

Contact Info

Product

Resources

About