Random Linear Network Coding for 5G Mobile Video Delivery

Vukobratović, Dejan; Tassi, Andrea; Delic, Savo; Khirallah, Chadi

doi:10.3390/info9040072

Cited by 20 publications

(7 citation statements)

References 76 publications

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“…The encoded data flow, guided by MIMO, dynamically adjusts its coding scheme in response to real-time system conditions, optimizing resource utilization and maximizing data throughput. [9]. The rapid expansion of IoT devices necessitates efficient data management, where network coding becomes indispensable.…”

Section: Related Workmentioning

confidence: 99%

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Julio,

Garcia,

Díaz

et al. 2023

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Julio,

Garcia,

Díaz

et al. 2023

IEEE Access

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“…There are several proposals leveraging the benefits of NC together with video delivery including [15], [16], [17] and [18]. Hierarchical Network Coding (HNC) technique is proposed in [15] for using NC with scalable video bit stream in CDNs.…”

Section: Related Workmentioning

confidence: 99%

“…Further, [16] and [17] investigate the use NC for the unequal error protection (UEP) for layered video delivery. Recently, [18] proposed the integration of network coding with 5G for mobile video delivery. However, all the existing works focus on the pre-programmed network coding models.…”

Section: Related Workmentioning

confidence: 99%

Nancy: Neural Adaptive Network Coding Methodology for Video Distribution Over Wireless Networks

Saxena

Naresh

Gupta

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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This paper presents NANCY, a system that generates adaptive bit rates (ABR) for video and adaptive network coding rates (ANCR) using reinforcement learning (RL) for video distribution over wireless networks. NANCY trains a neural network model with rewards formulated as quality of experience (QoE) metrics. It performs joint optimization in order to select: (i) adaptive bit rates for future video chunks to counter variations in available bandwidth and (ii) adaptive network coding rates to encode the video chunk slices to counter packet losses in wireless networks. We present the design and implementation of NANCY, and evaluate its performance compared to state-of-the-art video rate adaptation algorithms including Pensieve and robustMPC. Our results show that NANCY provides 29.91% and 60.34% higher average QoE than Pensieve and robustMPC, respectively.

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“…However, they are not forwarded in the conventional way. Instead, when the scheduler is ready to forward a packet from F D , it forwards a random linear combination, i.e.. network coding [21], [22], of all cohort k in the queue. Before forwarding, the packet header is modified to indicate that it is from cohort k and it is also tagged "high priority".…”

Section: A Overviewmentioning

confidence: 99%

COded Taking And Giving (COTAG): Enhancing Transport Layer Performance over Indoor Millimeter Wave Access Networks

Wu¹,

Huang²,

Ramanathan³

2019

Preprint

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Millimeter wave (mmWave) access networks have the potential to meet the high-throughput and low-latency needs of immersive applications. However, due to the highly directional nature of the mmWave beams and due to their susceptibility to human-induced blockage, the associated wireless communication links are vulnerable to large fluctuations in channel quality. These large fluctuations result in disproportionately adverse effects on performance of transport layer protocols such as Transmission Control Protocol (TCP). Furthermore, we show in this paper, that TCP continues to significantly under perform even when dual-connectivity is used to combat the effects of channel quality fluctuations. To overcome this challenge, we propose a network layer solution, Coded Taking and Giving (COTAG) scheme to sustain low-latency and high-throughput end-to-end TCP performance. In particular, COTAG creates network encoded packets at the network gateway and each mmWave access point (AP) aiming to adaptively take the spare bandwidth on each link in the network for packet transmission. Further, if one link does not have enough bandwidth, COTAG actively gives up the transmission opportunity via conditionally dropping packets. Consequently, the proposed COTAG actively adapts to changes in mmWave link quality and enhances the TCP performance without jeopardizing the latency of immersive connect delivery. To evaluate the effectiveness of the proposed COTAG, we conduct experiments and simulations using off-the-shelf APs and NS-2. The evaluation results show the proposed COTAG significantly improves end-to-end TCP performance.

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Random Linear Network Coding for 5G Mobile Video Delivery

Cited by 20 publications

References 76 publications

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Nancy: Neural Adaptive Network Coding Methodology for Video Distribution Over Wireless Networks

COded Taking And Giving (COTAG): Enhancing Transport Layer Performance over Indoor Millimeter Wave Access Networks

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