8,394 research outputs found
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
Historical information aware unequal error protection of scalable HEVC/H.265 streaming over free space optical channels
Free space optical (FSO) systems are capable of supporting high data rates between fixed points in the context of flawless video communications. Layered video coding facilitates the creation of different-resolution subset layers for variablethroughput transmission scenarios. In this paper, we propose Historical information Aware Unequal Error Protection (HAUEP) for the scalable high efficiency video codec (SHVC) used for streaming over FSO channels. Specifically, the objective function (OF) of the current video frame is designed based on historical information of its dependent frames. By optimizing this OF, specific subset layers may be selected in conjunction with carefully selected forward error correction (FEC) coding rates, where the expected video distortion is minimized and the required bitrate is reduced under the constraint of a specific throughput. Our simulation results show that the proposed system outperforms the traditional equal error protection (EEP) scheme by about 4.5 dB of Eb=N0 at a peak signal-to-noise ratio (PSNR) of 33 dB. From a throughput-oriented perspective, HA-UEP is capable of reducing the throughput to about 30% compared to that of the EEP benchmarker, while achieving an Eb=N0 gain of 4.5 dB
Resource Allocation Frameworks for Network-coded Layered Multimedia Multicast Services
The explosive growth of content-on-the-move, such as video streaming to
mobile devices, has propelled research on multimedia broadcast and multicast
schemes. Multi-rate transmission strategies have been proposed as a means of
delivering layered services to users experiencing different downlink channel
conditions. In this paper, we consider Point-to-Multipoint layered service
delivery across a generic cellular system and improve it by applying different
random linear network coding approaches. We derive packet error probability
expressions and use them as performance metrics in the formulation of resource
allocation frameworks. The aim of these frameworks is both the optimization of
the transmission scheme and the minimization of the number of broadcast packets
on each downlink channel, while offering service guarantees to a predetermined
fraction of users. As a case of study, our proposed frameworks are then adapted
to the LTE-A standard and the eMBMS technology. We focus on the delivery of a
video service based on the H.264/SVC standard and demonstrate the advantages of
layered network coding over multi-rate transmission. Furthermore, we establish
that the choice of both the network coding technique and resource allocation
method play a critical role on the network footprint, and the quality of each
received video layer.Comment: IEEE Journal on Selected Areas in Communications - Special Issue on
Fundamental Approaches to Network Coding in Wireless Communication Systems.
To appea
Zero-padding Network Coding and Compressed Sensing for Optimized Packets Transmission
Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything on a never-before-seen scale. Such networks, however, have to tackle the inherent issues created by the presence of very heterogeneous data transmissions over the same shared network. This very diverse communication, in turn, produces network packets of various sizes ranging from very small sensory readings to comparatively humongous video frames. Such a massive amount of data itself, as in the case of sensory networks, is also continuously captured at varying rates and contributes to increasing the load on the network itself, which could hinder transmission efficiency. However, they also open up possibilities to exploit various correlations in the transmitted data due to their sheer number. Reductions based on this also enable the networks to keep up with the new wave of big data-driven communications by simply investing in the promotion of select techniques that efficiently utilize the resources of the communication systems. One of the solutions to tackle the erroneous transmission of data employs linear coding techniques, which are ill-equipped to handle the processing of packets with differing sizes. Random Linear Network Coding (RLNC), for instance, generates unreasonable amounts of padding overhead to compensate for the different message lengths, thereby suppressing the pervasive benefits of the coding itself. We propose a set of approaches that overcome such issues, while also reducing the decoding delays at the same time. Specifically, we introduce and elaborate on the concept of macro-symbols and the design of different coding schemes. Due to the heterogeneity of the packet sizes, our progressive shortening scheme is the first RLNC-based approach that generates and recodes unequal-sized coded packets. Another of our solutions is deterministic shifting that reduces the overall number of transmitted packets. Moreover, the RaSOR scheme employs coding using XORing operations on shifted packets, without the need for coding coefficients, thus favoring linear encoding and decoding complexities.
Another facet of IoT applications can be found in sensory data known to be highly correlated, where compressed sensing is a potential approach to reduce the overall transmissions. In such scenarios, network coding can also help. Our proposed joint compressed sensing and real network coding design fully exploit the correlations in cluster-based wireless sensor networks, such as the ones advocated by Industry 4.0. This design focused on performing one-step decoding to reduce the computational complexities and delays of the reconstruction process at the receiver and investigates the effectiveness of combined compressed sensing and network coding
Video transmission over a relay channel with a compress-forward code design
There is an increasing demand to support high data rate multimedia applications over the current day wireless networks which are highly prone to errors. Relay channels, by virtue of their spatial diversity, play a vital role in meeting this demand without much change to the current day systems. A compress-forward relaying scheme is one of the exciting prospects in this regard owing to its ability to always outperform direct transmission. With regards to video transmission, there is a serious need to ensure higher protection for the source bits that are more important and sensitive. The objective of this thesis is to develop a practical scheme for transmitting video data over a relay channel using a compress-forward relaying scheme and compare it to direct and multi-hop transmissions. We also develop a novel scheme whereby the relay channel can be used as a means to provide the required unequal error protection among the MPEG-2 bit stream. The area of compress-forward (CF) relaying has not been developed much to date, with most of the research directed towards the decode-forward scheme. The fact that compress-forward relaying always ensures better results than direct transmission is an added advantage. This has motivated us to employ CF relaying in our implementation. Video transmission and streaming applications are being increasingly sought after in the current generation wireless systems. The fact that video applications are bandwidth demanding and error prone, and the wireless systems are band-limited and unreliable, makes this a challenging task. CF relaying, by virtue of their path diversity, can be considered to be a new means for video transmission. To exploit the above advantages, we propose an implementation for video transmission over relay channels using a CF relaying scheme. Practical gains in peak signal-to-noise ratio (PSNR) have been observed for our implementation compared to the simple binary-input additive white Gaussian noise (BIAWGN) and two-hop transmission scenarios
Media motion-based resource distribution for mobile video networking
Wireless video communication is challenging due to vulnerability of media bitstreams to channel distortions. Investigation has been carried out on wireless video channel under tight networking resource budget. One of the challenges is the impact of channel errors on the quality of media streams with high motion activity. Motion activity in this context defines the magnitude of activity displacement in video sequence. Based on the analysis, Media Motion-based Resource Distribution (MRD) is proposed to maximize the average received video quality over wireless system, by regulating the resource distribution of the media streams based on their motion activity characteristics. Experimental results demonstrate that the proposed scheme can improve the average received video quality performance under tight resource constraints budget.
Keywords: Wireless video communication, resource constraints, received video performance, media motio
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