Sliding-Window Forward Error Correction Based on Reference Order for Real-Time Video Streaming

Wang, Rui; Si, Liang; He, Bifeng

doi:10.1109/access.2022.3162217

Cited by 2 publications

(2 citation statements)

References 19 publications

(28 reference statements)

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“…Models that use Deep Reinforcement Learning (DRL) [14], Linear Optimizations [15], Online Bitrate Selection [16], Deep Learning Optimizations [17], cycle vector-quantized variational autoencoder (cycle-VQ-VAE) [18], Flexible Latency Aware Streaming (FLAS) [19], and Reinforcement Learning-Based Rate Adaptation (RLRA) [20], for dynamic control over streaming operations are discussed & evaluated under different scenarios. These models are further extended via the work in [21,22,23,24,25], which propose use of Shift-Tile-Tracking (STC), LSTM based streaming, scalable-high-efficiency-video-coding (SHVC) with device-to-device communications, Sliding-Window Forward Error Correction (SW FEC), and context-aware streaming, which enables real-time processing for different video types. Extended models that use Proactive Caching [26], and Video streaming based on super resolution [27] are also discussed, and are highly useful for a wide variety of real-time use cases.…”

Section: Literature Review Of Existing Streaming Modelsmentioning

confidence: 99%

DLLBVS: Design of a High-Efficiency Deep Learning based Low-BER Video Streaming Model for High-Noise Wireless Networks

Gowrishankar S., Ramesh Babu H. S, Prasad G. R.

2024

jes

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Design of video streaming models for high-noise networks is a multimodal process that requires efficient channel modelling, noise analysis, error-specific stream control, intelligent data augmentation, etc. This article proposes design of a high-efficiency deep learning based low-BER (Bit Error Rate) Video streaming model for high-noise wireless networks. The proposed model initially uses a Long-Short-Term Memory (LSTM) block for estimation of frame level features, and then uses Orthogonal Frequency Division Multiple Access (OFDMA) based modulation platform for transmission and reception of processed video frames. The OFDMA model is cascaded with a chaotic communication module, which assists in improving data fidelity under different noise conditions. The chaotic communication module is optimized using Grey Wolf Optimizer (GWO), which assists in setting up its hyperparameters for better efficiency under real-time communication scenarios. Video frames were pre-processed using Dual Neural Networks that assisted in estimation of differential frame information sets. Due to estimation of this differential frame information, streaming speed is improved, which assists in increasing number of frames transmitted per second, thereby improving streaming performance for different video types. This frame information is further processed by an iterative Gated Recurrent Unit (GRU) based VARMAx Model, which assists in predicting frame removal instances, thus assisting in faster & low error communications. The GRU VARMAx Model optimizes data flow, thus reducing congestion and improving throughput. The model was tested under AWGN (Additive While Gaussian Noise), Rayleigh, & Rician channel types, and its efficiency was compared with standard streaming techniques in terms of communication delay, Bit Error Rate, Peak to Average Power Ratio (PAPR), throughput, communication jitter and computational complexity levels. Based on this comparison it was observed that the proposed model showcased 3.5% lower communication delay, 8.3% lower BER, 5.9% lower PAPR, 10.5% higher throughput, 3.9% lower jitter and 6.4% lower computational complexity, which makes it highly useful for a wide variety of real-time streaming scenarios.

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Section: Literature Review Of Existing Streaming Modelsmentioning

confidence: 99%

DLLBVS: Design of a High-Efficiency Deep Learning based Low-BER Video Streaming Model for High-Noise Wireless Networks

Gowrishankar S., Ramesh Babu H. S, Prasad G. R.

2024

jes

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“…To develop a better QoE and better performance, recent studies have explored the advantages of the machine and DL architectures in AFEC for real-time video transmission in multi-path environments. Long Short-Term Memory (LSTM) [11] has recently attracted many researchers to embed these networks with AFECs. Despite implementing Deep Learning (DL) methods that improve performance, video streaming applications in multi-homed clients continue to suffer from packet losses, distortion, low PDR, and latency [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

Vijayashaarathi¹,

NithyaKalyani²

2023

Computer Systems Science and Engineering

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In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.

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Sliding-Window Forward Error Correction Based on Reference Order for Real-Time Video Streaming

Cited by 2 publications

References 19 publications

DLLBVS: Design of a High-Efficiency Deep Learning based Low-BER Video Streaming Model for High-Noise Wireless Networks

DLLBVS: Design of a High-Efficiency Deep Learning based Low-BER Video Streaming Model for High-Noise Wireless Networks

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

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