Speeding up Future Video Distribution via Channel-Aware Caching-Aided Coded Multicast

Future Internet usage will be dominated by the consumption of a rich variety of online multimedia services accessed from an exponentially growing number of multimedia capable mobile devices. As such, future Internet designs will be challenged to provide solutions that can deliver bandwidth-intensive, delay-sensitive, on-demand video-based services over increasingly crowded, bandwidth-limited wireless access networks. One of the main reasons for the bandwidth stress facing wireless network operators is the difficulty to exploit the multicast nature of the wireless medium when wireless users or access points rarely experience the same channel conditions or access the same content at the same time. In this paper, we present and analyze a novel wireless video delivery paradigm based on the combined use of channel-aware caching and coded multicasting that allows simultaneously serving multiple cache-enabled receivers that may be requesting different content and experiencing different channel conditions. To this end, we reformulate the caching-aided coded multicast problem as a joint source-channel coding problem and design an achievable scheme that preserves the cache-enabled multiplicative throughput gains of the error-free scenario,by guaranteeing per-receiver rates unaffected by the presence of receivers with worse channel conditions.Comment: 11 pages,6 figures,to appear in IEEE JSAC Special Issue on Video Distribution over Future Interne

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields

A Time-Efficient Strategy For Relay Selection and Link Scheduling In Wireless Communication Networks

Despite the unprecedented success and proliferation of wireless communication, sustainable reliability and stability among wireless users are still considered important issues in the underlying link protocols. Existing link-layer protocols, like ARQ [44] or HARQ [57,67] approaches are designed to achieve this goal by discarding a corrupted packet at the receiver and performing one or more retransmissions until the packet is successfully decoded or a maximum number of retransmission attempts is reached. These strategies suffer from degradation of throughput and overall system instability since packets need to be en/decode in every hop, leading to high burden for relay nodes especially when the traffic load is high. On the other hand, due to the broadcast nature of wireless communication, when a relay transmits a packet to a specific receiver, it could become interference to other receivers. Thus, rather than activating all the relays simultaneously, we can only schedule a subset of relays in each time slot such that the interference among the links will not cause some transmissions to fail. Accordingly, in this dissertation, we mainly address the following two problems: 1) Relay selection: given a route (i.e., a sequence of relays), how to select the relays to en/decode packets to minimize the latency to reach the destination? 2) Link scheduling: how to schedule relays such that the interference among the relays will not cause transmission failure and the throughput is maximized? Relay Selection Problem. To solve the relay selection problem, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency. CEDAR is the first theoretical framework for selecting en/decoding relays to minimize packet latency in wireless communication networks. It employs a theoretically-sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in packet\u27s route. To identify the intermediate relay nodes for en/decoding to minimize average packet latency, we mathematically analyze the average packet delay, using Finite State Markovian Channel model and priority queuing model, and then formalize the problem as a non-linear integer programming problem. To solve this problem, we design a scalable and distributed scheme which has very low complexity. The experimental results demonstrate that CEDAR is superior to the schemes using hop-by-hop decoding and destination-decoding in terms of both packet delay and throughput. In addition, the simulation results show that CEDAR can achieve the optimal performance in most cases. Link Scheduling Problem. As for the link scheduling problem, we formulate a new problem called Fading-Resistant Link Scheduling (Fadin-R-LS) problem, which aims to maximize the throughput (the sum data rate) for all the links in a single time slot. The problem is different from the existing link scheduling problems by incorporating the Rayleigh-fading model to describe the interference. This model extends the deterministic interference model based on the Signal-to-Interference Ratio (SIR) using stochastic propagation to address fading effects in wireless networks. Based on the geometric structure of Fadin-R-LS, we then propose three centralized schemes for Fadin-R-LS, with O(g(L)), O(g(L)), and O(1) performance guarantee for packet latency, where g(L) is the number of length magnitudes of link set L. Furthermore, we propose a completely distributed approach based on game theory, which has O(g(L)^2\alpha) performance guarantee. Furthermore, we incorporate a cooperative communication (CC) technique, e.g., maximum ratio combining (MRC), into our system to further improve the throughput, in which receivers are allowed to combine messages from different senders to combat transmission errors. In particular, we formulate two problems named cooperative link scheduling problem (CLS) and one-shot cooperative link scheduling problem (OCLS). The first problem aims to find a schedule of links that uses the minimum number of time slots to inform all the receivers. The second problem aims to find a set of links that can inform the maximum number of receivers in one time slot. We prove both problems to be NP-hard. As a solution, we propose an algorithm for both CLS and OCLS with g(K) approximation ratio, where g(K) is so called the diversity of key links. In addition, we propose a greedy algorithm with O(1) approximation ratio for OCLS when the number of links for each receiver is upper bounded by a constant. In addition, we consider a special case for the link scheduling problem, where there is a group of vehicles forming a platoon and each vehicle in the platoon needs to communicate with the leader vehicle to get the leader vehicle\u27s velocity and location. By leveraging a typical feature of a platoon, we devise a link scheduling algorithm, called the Fast and Lightweight Autonomous link scheduling algorithm (FLA), in which each vehicle determines its own time slot simply based on its distance to the leader vehicle. Finally, we conduct a simulation on Matlab to evaluate the performance of our proposed methods. The experimental results demonstrate the superior performance of our link scheduling methods over the previous methods