sparse network coding (SNC) is a promising technique for reducing the complexity of random linear networkcoding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC fo...
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sparse network coding (SNC) is a promising technique for reducing the complexity of random linear networkcoding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the performance of ADD and propose a Markov chain model to analyze this SNC metric. This model provides a lower bound for decoding delay of a generation as well as a lower bound for decoding delay of a portion of a generation. Results show that although RLNC provides a better decoding delay of an entire generation, SNC outperforms RLNC in terms of ADD per packet. Sparsity of the coefficient matrix is a key parameter for ADD per packet to transmit stream data. The proposed model enables us to select the appropriate degree of sparsity based on the required ADD. Numerical results validate that the proposed model would enable a precise evaluation of SNC technique behavior.
networkcoding is an elegant and novel technique to improve network throughput and performance. It is considered as a critical technology to facilitate ever-increasing demands of future wireless networks. It exploits ...
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networkcoding is an elegant and novel technique to improve network throughput and performance. It is considered as a critical technology to facilitate ever-increasing demands of future wireless networks. It exploits the broadcast nature of wireless media and cooperatively codes packets from different senders to provide reliable, secure, and efficient transmissions. Current research focuses on either transmission delay, coding complexity, forwarding security, or end-to-end throughput. networkcoding-aided solutions can recover lost packets without feedback, eliminate latency, reduce the routing cost on diverse paths, or optimize the capacity of unstable wireless networks. However, devices or smart sensors usually have limited computational capacity and some applications could not tolerate high decoding delay, which prevents networkcoding from being widely deployed in the real world. In recent years, many research methods consider simplifying decoding matrix or coding algorithm to alleviate the shortcoming of networkcoding and further satisfy the extreme demands of the future wireless network. This article summarizes complexity-optimized methods and explains the interaction effect of coding opportunities and decoding overhead. We propose a taxonomy of practical networkcoding methods and illustrate three practical directions on cutting computational complexity and enhancing progressive decoding. We also conclude the benefit and cost of current networkcoding algorithms along with the outline of future research.
One of the by-products of sparse network coding (SNC) is the ability to perform partial decoding, i.e., decoding some original packets prior to collecting all needed coded packets to decode the entire coded data. Due ...
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One of the by-products of sparse network coding (SNC) is the ability to perform partial decoding, i.e., decoding some original packets prior to collecting all needed coded packets to decode the entire coded data. Due to this ability, SNC has been recently used as a technique for reducing the Average Decoding Delay (ADD) per packet in real-time multimedia applications. This study focuses on characterizing the ADD per packet for SNC considering the impact of finite field size. We present a Markov Chain model that allows us to determine lower bounds on the mean number of transmissions required to decode a fraction of a generation and the ADD per packet of the generation. We validate our model using simulations and show that the smaller finite fields, e.g., q = 24, outperform large finite fields, e.g., q = 232, in regard to the ADD per packet and provide a better tradeoff between the ADD per packet and the overall number of transmissions to decode a generation.
Packet transmissions over multi-hop lossy links are important in the future space-air-ground integrated networks. This letter considers sparse coded transmissions with reduced complexity compared to the well-known ran...
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Packet transmissions over multi-hop lossy links are important in the future space-air-ground integrated networks. This letter considers sparse coded transmissions with reduced complexity compared to the well-known random linear networkcoding, which is known to be efficient in multi-hop lossy links. We propose a reinforcement learning framework for dynamically adjusting coding parameters on-line to improve the performance based on decoder feedback. A key advantage of the dynamic scheme is that it does not require a prior knowledge of the environment nor an analysis model. Extensive evaluation shows that it adapts well in scenarios where link conditions are unknown and/or changing, and achieves performance close to that of the optimal fixed schemes found by exhaustive search.
Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliabi...
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Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliability of communications is ensured via different random linear networkcoding (RLNC) techniques. We deal with a fundamental problem: the computational complexity of the RLNC decoder. The higher the number of decoding operations is, the more the user's computational overhead grows and, consequently, the faster the battery of mobile devices drains. By referring to several sparse RLNC techniques, and without any assumption on the implementation of the RLNC decoder in use, we provide an efficient way to characterize the performance of users targeted by ultra-reliable layered multicast services. The proposed modeling allows to efficiently derive the average number of coded packet transmissions needed to recover one or more service layers. We design a convex resource allocation framework that allows to minimize the complexity of the RLNC decoder by jointly optimizing the transmission parameters and the sparsity of the code. The designed optimization framework also ensures service guarantees to predetermined fractions of users. The performance of the proposed optimization framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video services.
Multicast services are believed to play a relevant role in next wireless networking scenarios. In this paper we exploit Tunable sparse network coding techniques to increase reliability of multicast communications. We ...
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ISBN:
(纸本)9781538617342
Multicast services are believed to play a relevant role in next wireless networking scenarios. In this paper we exploit Tunable sparse network coding techniques to increase reliability of multicast communications. We show that the proposed networkcoding scheme yields a better performance than state-of-the-art solutions, which are traditionally based on retransmissions. We first use a model to analytically compare the two approaches. Then, we validate and broaden this analysis by means of an experimental campaign over a testbed deployed with Commercial Of-The-Shelf devices. This platform, comprising low cost devices (Raspberry-PI), allows us to assess the feasibility of the proposed solution, which offers a relevant gain in terms of performance.
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