Adaptive video-aware forward error correction code allocation for reliable video transmission

Hussain, Mushahid; Hameed, Abdul

doi:10.1007/s11760-017-1142-3

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…Suppose that frames I, P1, and P2 correspond to 8, 4, and 4 packets, respectively (the frames are divided into packets for transmission based on the maximum transmission unit (MTU)). Under the conditions of RS coding, we use RS (16,8) to encode Window-1 to generate 8 repair packets. For frame P1, RS (16,12) is used on Window-2 to generate 4 repair packets; for frame P2, RS (20,16) is used on Window-3 to generate 4 repair packets.…”

Section: B the Problem With Time-order Coding Window Managementmentioning

confidence: 99%

“…In addition, the recovered packets of the current frame cannot help to recover the lost packets of previous frames, and the The associate editor coordinating the review of this manuscript and approving it for publication was Zilong Liu . distortion of the previous frames may propagate to the current and following frames. In GOP-level FEC, the FEC coding window contains all the video frames in a GOP to produce the corresponding FEC redundancies [7], [8]. By utilizing a large coding window, GOP-level FEC is capable of handling burst loss.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Si²,

He³

2022

IEEE Access

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In real-time video streaming, data packets are transported over the network from a transmitter to a receiver. The quality of the received video fluctuates as the network conditions change, and it can degrade substantially when there is considerable packet loss. Forward error correction (FEC) techniques can be used to recover lost packets by incorporating redundant data. Conventional FEC schemes do not work well when scalable video coding (SVC) is adopted. In this paper, we propose a novel FEC scheme that overcomes the drawbacks of these schemes by considering the reference picture structure of SVC and weighting the reference pictures more when FEC redundancy is applied. The experimental results show that the proposed FEC scheme outperforms conventional FEC schemes.INDEX TERMS Forward error correction, real-time video streaming, scalable video coding, error correction codes, reference order.

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Section: B the Problem With Time-order Coding Window Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Si²,

He³

2022

IEEE Access

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“…The authors [31] analyzed several FEC approaches for error-free transmission of videos in varying network scenarios. RS encoding is applied at the packet, frame, and sub-Groupof-Pictures (GoP) level by considering various attributes of the videos.…”

Section: Related Studiesmentioning

confidence: 99%

VQProtect: Lightweight Visual Quality Protection for Error-Prone Selectively Encrypted Video Streaming

Gillani

Asghar

Shifa

et al. 2022

Entropy

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Mobile multimedia communication requires considerable resources such as bandwidth and efficiency to support Quality-of-Service (QoS) and user Quality-of-Experience (QoE). To increase the available bandwidth, 5G network designers have incorporated Cognitive Radio (CR), which can adjust communication parameters according to the needs of an application. The transmission errors occur in wireless networks, which, without remedial action, will result in degraded video quality. Secure transmission is also a challenge for such channels. Therefore, this paper’s innovative scheme “VQProtect” focuses on the visual quality protection of compressed videos by detecting and correcting channel errors while at the same time maintaining video end-to-end confidentiality so that the content remains unwatchable. For the purpose, a two-round secure process is implemented on selected syntax elements of the compressed H.264/AVC bitstreams. To uphold the visual quality of data affected by channel errors, a computationally efficient Forward Error Correction (FEC) method using Random Linear Block coding (with complexity of O(k(n−1)) is implemented to correct the erroneous data bits, effectively eliminating the need for retransmission. Errors affecting an average of 7–10% of the video data bits were simulated with the Gilbert–Elliot model when experimental results demonstrated that 90% of the resulting channel errors were observed to be recoverable by correctly inferring the values of erroneous bits. The proposed solution’s effectiveness over selectively encrypted and error-prone video has been validated through a range of Video Quality Assessment (VQA) metrics.

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“…Several variations of the MDC concept have been offered in the research literature [38] and four of the pertinent implementations are Sub-Sample, Quantisation, Transform and Forward Error Correction. We will primarily focus on Forward Error Correction (FEC) [39] as it provides a means of dynamic adaptive stream encoding, low computational complexity, the large quantity of descriptions necessary for the substantial numbers of heterogeneous media streaming devices indicated for future deployment, and it has attracted considerable attention in the literature [40][41][42][43]. Ref.…”

Section: Description-based Streaming Modelsmentioning

confidence: 99%

Efficient Delivery of Scalable Video Using a Streaming Class Model

2018

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When we couple the rise in video streaming with the growing number of portable devices (smart phones, tablets, laptops), we see an ever-increasing demand for high-definition video online while on the move. Wireless networks are inherently characterised by restricted shared bandwidth and relatively high error loss rates, thus presenting a challenge for the efficient delivery of high quality video. Additionally, mobile devices can support/demand a range of video resolutions and qualities. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to available bandwidth and heterogeneity, and can provide a graceful changes in video quality, all while respecting viewing satisfaction. In this context, the use of well-known scalable/layered media streaming techniques, commonly known as scalable video coding (SVC), is an attractive solution. SVC encodes a number of video quality levels within a single media stream. This has been shown to be an especially effective and efficient solution, but it fares badly in the presence of datagram losses. While multiple description coding (MDC) can reduce the effects of packet loss on scalable video delivery, the increased delivery cost is counterproductive for constrained networks. This situation is accentuated in cases where only the lower quality level is required. In this paper, we assess these issues and propose a new approach called Streaming Classes (SC) through which we can define a key set of quality levels, each of which can be delivered in a self-contained manner. This facilitates efficient delivery, yielding reduced transmission byte-cost for devices requiring lower quality, relative to MDC and Adaptive Layer Distribution (ALD) (42% and 76% respective reduction for layer 2), while also maintaining high levels of consistent quality. We also illustrate how selective packetisation technique can further reduce the effects of packet loss on viewable quality by leveraging the increase in the number of frames per group of pictures (GOP), while offering a means of reducing overall error correction and by providing equality of data in every packet transmitted per GOP.

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Adaptive video-aware forward error correction code allocation for reliable video transmission

Cited by 13 publications

References 21 publications

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

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

VQProtect: Lightweight Visual Quality Protection for Error-Prone Selectively Encrypted Video Streaming

Efficient Delivery of Scalable Video Using a Streaming Class Model

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