RaptorQ Code Research Articles

Fountain Codes for Reliable and Deterministic Packet Transmission in Industrial Cloud Control Systems

With the thriving of digitalization in industry and the rapid development of cloud computing and Internet of Things (IoT), it has become a growing trend to integrate industrial control with the cloud platform to form industrial cloud control systems (ICCSs). However, ICCSs bring great challenges to legacy communication networks, especially, wireless networks, on reliability and determinacy. This article utilizes fountain code as application-layer coding to provide high-reliability low-latency deterministic communication for ICCSs. According to the simulation results, fountain code scheme, especially, RaptorQ code significantly improves communication reliability. The performance difference of fountain codes between time-critical use cases and nontime-critical use cases provides another perspective to analyze and evaluate fountain codes. Then, we propose a self-adaptive coding parameter selection method based on different industrial use cases, to better meet the communication requirements and reduce the coding cost.

Efficient Multisource Data Delivery in Edge Cloud With Rateless Parallel Push

As the key infrastructure for emerging 5G and Internet-of-Things (IoT) applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this article proposes a multisource-aware adaptive data transmission solution, Parallel Push (PPUSH), by leveraging the fact that data objects in the cloud are generally replicated among servers by design. At the high level, PPUSH achieves efficient delivery of multisource data by launching multiple push flows in parallel; and at the low level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, push is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, PPUSH could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by $1.495\times $ and $1.329\times $ , respectively, demonstrating a total improvement of $1.586\times $ , when enabled at the same time.

Efficient Mobile Video Streaming via Context-Aware RaptorQ-Based Unequal Error Protection

Mobile video streaming systems typically apply the forward error correction (FEC) at the application layer to cope with packet-level transmission errors, which complements the bit-level correction mechanisms at the physical layer. However, most existing works fail to exploit the block-level dependencies in both intra and interframe coding modes of a single-layer compressed video, and thus are less efficient for the prevailing H.264/AVC and/or H.265/HEVC compatible single-layer video application. To this end, we propose a low-complexity FEC, i.e., context-aware RaptorQ (CA-RQ) with unequal error protection (UEP), to improve the error recovery performance of the single-layer mobile video streaming, through incorporating the block-level dependencies in the compressed video data. We use a packet-level video transmission distortion model that considers the dependencies in both spatial and temporal domains, to quantify the importance of video packets within a group of pictures (GoP). The compressed video packets are categorized and grouped into several classes according to their importance to construct the CA-RQ code with the UEP property. We provide a theoretical analysis on redundancy allocation bounds to demonstrate the superior performance of proposed CA-RQ over the standard RaptorQ code. In the meantime, extensive simulations have shown that our scheme not only offers much better subjective visual quality with less than 50% additional redundant symbols as compared to the Macroblock-Based UEP (MB-UEP) scheme, but also outperforms the MB-UEP and classical equal error protection (EEP)-based schemes, by a 0.45% $\sim$ 5.71% and 0.94% $\sim$ 6.78% margin, respectively, in reconstructed quality evaluated using the structural similarity (SSIM) index, across a reasonable range of redundancy proportions.

RaptorQ-Based Efficient Multimedia Transmission Over Cooperative Cellular Cognitive Radio Networks

Accommodating the fast-growing multimedia traffic demands at low cost is critical to ensure the future competitiveness of cellular networks. Cognitive radio (CR) emerges as a key technology to enhance radio spectrum utilization and provides an effective solution to meet critical spectrum demands for future mobile multimedia traffics. In this paper, we investigate reliable downlink video transmission in underlay cellular CR networks by means of cooperative diversity. We use RaptorQ code as the Application Layer Forward Error Correction scheme to combat packet loss arising from the primary user interference and other channel conditions such as channel fading. For implementing cooperative diversity, we select the best relay based on the ability of successful decoding using RaptorQ and the value of signal-to-interference-plus-noise ratio at the destination node. Moreover, for efficient primary user channel utilization, the selected relay performs merging of the encoding coefficients before forwarding to the destination. We evaluate the performance of the proposed framework under various time sharing scenarios between the direct and best indirect transmission links in terms of decoding overhead, probability of decoding failure, and peak signal-to-noise ratio of the received videos. Numerical results show that the proposed scheme outperforms the baseline transmission scheme and conventional relaying schemes using Amplify-and-Forward and Decode-and-Forward transmission techniques.

Bounds on the Reliability of RaptorQ Codes in the Finite-Length Regime

In this paper we analyze the bounds on the reliability of RaptorQ codes under maximum likelihood decoding, especially in the finite-length regime. RaptorQ code ensembles by a high-order low density generator-matrix (LDGM) code as pre-code and an inner Luby transform (LT) code. By investigating the rank of the product of two random coefficient matrices of the high-order LDGM code and the inner LT code, we derive the expressions for the upper and lower bounds of decoding failure probability (DFP) on the RaptorQ code. Then, the accuracy of our derived theoretical bounds are verified through the Monte Carlo simulations with different degree distributions and short packets. The high accuracy bounds are then exploited to design near-optimum finite-length RaptorQ codes, enabling a tight control on the tradeoff between decoding complexity and DFP.

Reducing the decoding complexity of RaptorQ codes for delay sensitive applications using a simplified and scaled-down matrix

RaptorQ codes, a class of fountain codes, are widely used as a way to achieve forward error correction at the application layer. Whereas RaptorQ codes perform impressively in terms of symbol recovery, its high computational complexity limits its applicability in demanding real-time scenarios. As a way to resolve this inefficiency, we propose using a novel matrix structure designed to reduce the decoding complexity of RaptorQ codes. Specifically, we replace Luby Transform codes and Low-density Parity Check (LDPC) codes in RaptorQ code operations using a novel binary matrix based on Kolchin’s Theorem. Our proposed improvements remove the need for LDPC codes to decrease the dimension of the matrix, and it reduces the latency resulting from matrix inversions. Given that the resulting latency from this process dominates the entire RaptorQ code decoding process, our changes offer the potential for reducing the latency dramatically. Based on an extensive set of simulations using our proposed matrix structure under various configurations, we show that the proposed decoding latency is faster than that of RaptorQ codes, while maintaining an at-par decoding-failure probability.

Index-based maximum likelihood RaptorQ code decoder

Index-based maximum likelihood RaptorQ code decoder