296 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
An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems
The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station
Optimized Network-coded Scalable Video Multicasting over eMBMS Networks
Delivery of multicast video services over fourth generation (4G) networks
such as 3GPP Long Term Evolution-Advanced (LTE-A) is gaining momentum. In this
paper, we address the issue of efficiently multicasting layered video services
by defining a novel resource allocation framework that aims to maximize the
service coverage whilst keeping the radio resource footprint low. A key point
in the proposed system mode is that the reliability of multicast video services
is ensured by means of an Unequal Error Protection implementation of the
Network Coding (UEP-NC) scheme. In addition, both the communication parameters
and the UEP-NC scheme are jointly optimized by the proposed resource allocation
framework. Numerical results show that the proposed allocation framework can
significantly increase the service coverage when compared to a conventional
Multi-rate Transmission (MrT) strategy.Comment: Proc. of IEEE ICC 2015 - Mobile and Wireless Networking Symposium, to
appea
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
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