Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-free Transmission

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold. First, to systematically investigate the benefits of UEP+RLNC layered approach in servicing users with different reception capabilities. Second, to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study `expected percentage of decoded frames' as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.Comment: 15 pages, 12 figures, 3 tables, Under 2nd round of review, IEEE Transactions on Communication

Non-stationary resource allocation policies for delay-constrained video streaming: Application to video over internet-of-things-enabled networks

Due to the high bandwidth requirements and stringent delay constraints of multi-user wireless video transmission applications, ensuring that all video senders have sufficient transmission opportunities to use before their delay deadlines expire is a longstanding research problem. We propose a novel solution that addresses this problem without assuming detailed packet-level knowledge, which is unavailable at resource allocation time (i.e. prior to the actual compression and transmission). Instead, we translate the transmission delay deadlines of each sender's video packets into a monotonically-decreasing weight distribution within the considered time horizon. Higher weights are assigned to the slots that have higher probability for deadline-abiding delivery. Given the sets of weights of the senders' video streams, we propose the low-complexity Delay-Aware Resource Allocation (DARA) approach to compute the optimal slot allocation policy that maximizes the deadline-abiding delivery of all senders. A unique characteristic of the DARA approach is that it yields a non-stationary slot allocation policy that depends on the allocation of previous slots. This is in contrast with all existing slot allocation policies such as round-robin or rate-adaptive round-robin policies, which are stationary because the allocation of the current slot does not depend on the allocation of previous slots. We prove that the DARA approach is optimal for weight distributions that are exponentially decreasing in time. We further implement our framework for real-time video streaming in wireless personal area networks that are gaining significant traction within the new Internet-of-Things (IoT) paradigm. For multiple surveillance videos encoded with H.264/AVC and streamed via the 6tisch framework that simulates the IoT-oriented IEEE 802.15.4e TSCH medium access control, our solution is shown to be the only one that ensures all video bitstreams are delivered with acceptable quality in a deadline-abiding manner. © 1983-2012 IEEE

Distributed Detection in Energy Harvesting Wireless Sensor Networks

A conventional wireless sensor networks (WSN), consisting of sensors powered by nonrechargeable batteries, has a strictly limited lifetime. Energy harvesting (EH) from the environment is a promising solution to address the energy constraint problem in conventional WSNs, and to render these networks to self-sustainable networks with perpetual lifetimes. In EH-powered WSNs, where sensors are capable of harvesting and storing energy, power control is necessary to balance the rates of energy harvesting and energy consumption for data transmission. In addition, wireless communication channels change randomly in time due to fading. These together prompt the need for developing new power control strategies for an EH-enabled transmitter that can best exploit and adapt to the random energy arrivals and time-varying fading channels. We consider parallel structure EH-powered WSNs tasked with solving a binary distributed detection problem. Sensors process locally their observations, adapt their transmission according to the battery and fading channel states, and transmit their data symbols to the fusion center (FC) over orthogonal fading channels. We study adaptive transmission schemes that optimize detection performance metrics at the FC, subject to certain battery and transmit power constraints. In the first part, modeling the random energy arrival as a Poisson process, we propose a novel transmit power control strategy that is parameterized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals and we find the jointly optimal quantization thresholds and the scale factors such that detection metric at the FC is maximized. We have assumed that the battery operates at the steady-state and the energy arrival and channel models are independent and identically distributed across transmission blocks. In the second part, we assume the battery is not at the steady-state and both the channel and the energy arrival are modeled as homogeneous finite-state Markov chains. Therefore, the power control optimization problem at hand becomes a multistage stochastic optimization problem and can be solved via the Markov decision process (MDP) framework. This is the first work that develops MDP-based channel-dependent power control policy for distributed detection in EH-powered WSNs

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi