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

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November 11-16, 2018, Dallas, TX, US

Final Research Report for Sound Design and Audio Player

This deliverable describes the work on Task 4.3 Algorithms for sound design and feature developments for audio player. The audio player runs on the in-store player (ISP) and takes care of rendering the music playlists via beat-synchronous automatic DJ mixing, taking advantage of the rich musical content description extracted in T4.2 (beat markers, structural segmentation into intro and outro, musical and sound content classification). The deliverable covers prototypes and final results on: (1) automatic beat-synchronous mixing by beat alignment and time stretching – we developed an algorithm for beat alignment and scheduling of time-stretched tracks; (2) compensation of play duration changes introduced by time stretching – in order to make the playlist generator independent of beat mixing, we chose to readjust the tempo of played tracks such that their stretched duration is the same as their original duration; (3) prospective research on the extraction of data from DJ mixes – to alleviate the lack of extensive ground truth databases of DJ mixing practices, we propose steps towards extracting this data from existing mixes by alignment and unmixing of the tracks in a mix. We also show how these methods can be evaluated even without labelled test data, and propose an open dataset for further research; (4) a description of the software player module, a GUI-less application to run on the ISP that performs streaming of tracks from disk and beat-synchronous mixing. The estimation of cue points where tracks should cross-fade is now described in D4.7 Final Research Report on Auto-Tagging of Music.EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC D

Minimizing the impact of delay on live SVC-based HTTP adaptive streaming services

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming services. Video content is temporally split into segments which are offered at multiple qualities to the clients. These clients autonomously select the quality layer matching the current state of the network through a quality selection heuristic. Recently, academia and industry have begun evaluating the feasibility of adopting layered video coding for HAS. Instead of downloading one file for a certain quality level, scalable video streaming requires downloading several interdependent layers to obtain the same quality. This implies that the base layer is always downloaded and is available for playout, even when throughput fluctuates and enhancement layers can not be downloaded in time. This layered video approach can help in providing better service quality assurance for video streaming. However, adopting scalable video coding for HAS also leads to other issues, since requesting multiple files over HTTP leads to an increased impact of the end-to-end delay and thus on the service provided to the client. This is even worse in a Live TV scenario where the drift on the live signal should be minimized, requiring smaller segment and buffer sizes. In this paper, we characterize the impact of delay on several measurement-based heuristics. Furthermore, we propose several ways to overcome the end-to-end delay issues, such as parallel and pipelined downloading of segment layers, to provide a higher quality for the video service

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and Tutorials (revised Jan. 2018