Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks

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

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Control of Distributed Servers for Quality-Fair Delivery of Multiple Video Streams

International audienceThis paper proposes a quality-fair video delivery system able to transmit several encoded video streams to mobile users sharing some wireless resource. Video quality fairness, as well as similar delivery delay is targeted among streams. The proposed control system is implemented within some aggregator located near the bottleneck of the network. This is done by allocating the transmission rate among streams based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server, or directly evaluated by each server in a distributed way. Each encoding rate target is adjusted for each stream independently based on the corresponding buffering delay in the aggregator. The transmission and encoding rate control problems are addressed with a control-theoretic perspective. The system is described with a multi-input multi-output model and several Proportional Integral (PI) controllers are used to adjust the video quality as well as the buffering delay. The study of the system equilibrium and stability provides guidelines for choosing the parameters of the PI controllers. Experimental results show that better quality fairness is obtained compared to classical transmission rate fair streaming solutions while keeping similar buffering delays

Video transport optimization techniques design and evaluation for next generation cellular networks

Video is foreseen to be the dominant type of data traffic in the Internet. This vision is supported by a number of studies which forecast that video traffic will drastically increase in the following years, surpassing Peer-to-Peer traffic in volume already in the current year. Current infrastructures are not prepared to deal with this traffic increase. The current Internet, and in particular the mobile Internet, was not designed with video requirements in mind and, as a consequence, its architecture is very inefficient for handling this volume of video traffic. When a large part of traffic is associated to multimedia entertainment, most of the mobile infrastructure is used in a very inefficient way to provide such a simple service, thereby saturating the whole cellular network, and leading to perceived quality levels that are not adequate to support widespread end user acceptance. The main goal of the research activity in this thesis is to evolve the mobile Internet architecture for efficient video traffic support. As video is expected to represent the majority of the traffic, the future architecture should efficiently support the requirements of this data type, and specific enhancements for video should be introduced at all layers of the protocol stack where needed. These enhancements need to cater for improved quality of experience, improved reliability in a mobile world (anywhere, anytime), lower exploitation cost, and increased flexibility. In this thesis a set of video delivery mechanisms are designed to optimize the video transmission at different layers of the protocol stack and at different levels of the cellular network. Upon the architectural choices, resource allocation schemes are implemented to support a range of video applications, which cover video broadcast/multicast streaming, video on demand, real-time streaming, video progressive download and video upstreaming. By means of simulation, the benefits of the designed mechanisms in terms of perceived video quality and network resource saving are shown and compared to existing solutions. Furthermore, selected modules are implemented in a real testbed and some experimental results are provided to support the development of such transport mechanisms in practice

Timely information sharing in communication constrained systems

In the near future it is envisaged that there will be a proliferation of disruptive applications combining sensing capabilities with cutting-edge wireless technologies. The wide deployment and availability of sensing nodes as well as the large amounts of data being collected will call for the design of "smarter" ways of gathering and processing such data. Such networks will be driven by the need to extract the most relevant data/information that is of interest to possibly multiple agents/nodes, while optimizing the allocation of the shared limited available resources to adapt to the heterogeneity of the interests of the different nodes as well as the heterogeneity in their network conditions. Indeed, the timely sharing of relevant information is shaping to be crucial in applications where real-time decisions are to be constantly made. In the automotive industry for example, it is expected that vehicles equipped with sensing nodes could collaborate by sharing sensed information which would allow for vehicles with obstructed views to make safer decisions and/or enhance the capacity of roadways. In the infotainment industry, and more specifically in the case of Virtual Reality (VR) applications that require large amounts of data to be constantly streamed, users at proximity of each other and that are part of the same VR experience may largely benefit from sharing resources such as edge caches that could be leveraged for the timely computation and delivery of the needed data especially in settings where different users may request the same data. The optimization of information sharing in communication constrained systems will thus be a fundamental problem underlying such systems. The focus of our work is on the modeling and analysis of these classes of problems and their implications in practical sensing systems. This thesis is composed of two main parts. In the first one, we explore the optimization of applications where timely sharing of information leads to enhanced safety and more accurate real-time decisions. We investigate novel metrics and algorithms aimed at achieving a high degree of real-time situational awareness in applications with distributed sensing nodes. In the second part of this thesis, we explore the timely sharing of information in a multi-user VR setting that would provide immersive VR experiences among the users. In particular, we explore how to support 360° VR video applications where the prediction of users' future viewings orientation is a major component as well as how to leverage overlaps in users' predictions in order to relieve the load on the shared communication network resources. Such applications face major challenges mainly tied to the heterogeneity in users' devices, predictions, network conditions, etc., which we propose to tackle through the careful design of metrics and policies robust to such heterogeneity and aimed at enhancing while achieving fair VR performance among the users.Electrical and Computer Engineerin