An adaptive Forward Error Correction (FEC) scheme for MPQUIC Datagram that reuses QUIC loss detection signals and an off-the-shelf Reed-Solomon coding library to provide practical latency and reliability gains under the measured vehicular mobile conditions.
Abstract
Multipath transport has long been studied to improve robustness over heterogeneous networks, and Multipath QUIC (MPQUIC) is particularly attractive for low-latency applications because QUIC Datagram enables transmission without retransmissions. However, this advantage comes with a key drawback: QUIC Datagram is vulnerable to packet loss, especially in vehicular cellular environments with handovers and rapidly varying radio conditions. While prior studies have explored adaptive-FEC and coded multipath designs, practical deployment remains challenging when such mechanisms require substantial modifications to the transport stack or scheduler. In this paper, we present an adaptive Forward Error Correction (FEC) scheme for MPQUIC Datagram that reuses QUIC loss detection signals and an off-the-shelf Reed-Solomon (RS) coding library. The sender estimates smoothed per-path loss rates from QUIC loss signals and adaptively determines the number of parity packets for each FEC block according to the expected packet loss. We implement the proposed scheme on an existing MPQUIC stack and evaluate it through vehicular field experiments over three commercial LTE/5G paths. Under the main 4.0 Mbps setting, the proposed scheme improves both reliability and latency compared with No-FEC: the average one-way delay is reduced from 103.0 ms to 70.8 ms, the 95th-percentile delay from 281.2 ms to 142.3 ms, and the packet loss rate from 1.7 % to 0.8 %, with an average coding rate of 0.94. These results indicate that scheduler-agnostic adaptive-FEC can provide practical latency and reliability gains for MPQUIC Datagram under the measured vehicular mobile conditions.
This work proposes a joint Medium Access Control and Network layer design, centered on a distributed Table-Based (TB) routing protocol that removes control-plane signaling by leveraging only user-plane data transmissions for route discovery and maintenance, and extends TB with a decentralized Multi-Agent Deep Reinforce...
Sara Cavallero, Lorenzo Mario Amorosa, Giampaolo Cuozzo et al.· IEEE Access· 0 citations
Experimental results demonstrate that PSN-PATH significantly outperforms existing solutions, reducing FCT by 95.81%, enhancing goodput by 5.11×, and reducing retransmissions by over 99% under high-load, lossy conditions.
Zhe-Xiong Li, Shugui Wei, Puyu Zhao et al.· Conference on Applications,...· 0 citations
This paper investigates a packetized batteryless V2X overlay architecture in which a dedicated short-range communications (DSRC)-based packet simultaneously carries conventional V2X data and a passive overlay payload, and proposes a multi-agent reinforcement learning (MARL)-based adaptive parameter-selection method.
Zhao-Yu Liu, Ruikang Li, Liu Cao et al.· 0 citations
LoRa-LQE, a link quality estimation scheme specifically designed for mobile environments, outperforms state-of-the-art solutions and achieves packet delivery ratio improvements and energy efficiency improvements, thereby enabling more reliable communication for mobile IoT assets.
Geraldo A. Sarmento, T. R. D. Da Silva, Fernando J. V. Santos et al.· Journal of Internet Services...· 0 citations
The rapid evolution of 5G and emerging 6G networks requires optical access systems to support immersive extended reality (XR) services with stringent quality-of-service (QoS) requirements, like ultra-low latency and high bandwidth. However, conventional dynamic bandwidth allocation (DBA) schemes in passive optical netw...
Akhilesh Patel, Y. Singh· IEEE Transactions on Network...· 0 citations
It is concluded that maximizing Wi-Fi 7 performance requires precise parameter tuning adjusted to specific traffic types, network topologies, and utilized mechanisms.
Marek Natkaniec, Jakub Kogut· Applied Sciences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.