Cross-layer optimization in TCP/IP networks

TCP-AQM can be interpreted as distributed primal-dual algorithms to maximize aggregate utility over source rates. We show that an equilibrium of TCP/IP, if exists, maximizes aggregate utility over both source rates and routes, provided congestion prices are used as link costs. An equilibrium exists if and only if this utility maximization problem and its Lagrangian dual have no duality gap. In this case, TCP/IP incurs no penalty in not splitting traffic across multiple paths. Such an equilibrium, however, can be unstable. It can be stabilized by adding a static component to link cost, but at the expense of a reduced utility in equilibrium. If link capacities are optimally provisioned, however, pure static routing, which is necessarily stable, is sufficient to maximize utility. Moreover single-path routing again achieves the same utility as multipath routing at optimality

Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization

This paper considers a cross-layer optimization problem driven by multi-timescale stochastic exogenous processes in wireless communication networks. Due to the hierarchical information structure in a wireless network, a mixed timescale stochastic iterative algorithm is proposed to track the time-varying optimal solution of the cross-layer optimization problem, where the variables are partitioned into short-term controls updated in a faster timescale, and long-term controls updated in a slower timescale. We focus on establishing a convergence analysis framework for such multi-timescale algorithms, which is difficult due to the timescale separation of the algorithm and the time-varying nature of the exogenous processes. To cope with this challenge, we model the algorithm dynamics using stochastic differential equations (SDEs) and show that the study of the algorithm convergence is equivalent to the study of the stochastic stability of a virtual stochastic dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we derive a sufficient condition for the algorithm stability and a tracking error bound in terms of the parameters of the multi-timescale exogenous processes. Based on these results, an adaptive compensation algorithm is proposed to enhance the tracking performance. Finally, we illustrate the framework by an application example in wireless heterogeneous network

Online Reinforcement Learning for Dynamic Multimedia Systems

In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table

On distributed scheduling in wireless networks exploiting broadcast and network coding

In this paper, we consider cross-layer optimization in wireless networks with wireless broadcast advantage, focusing on the problem of distributed scheduling of broadcast links. The wireless broadcast advantage is most useful in multicast scenarios. As such, we include network coding in our design to exploit the throughput gain brought in by network coding for multicasting. We derive a subgradient algorithm for joint rate control, network coding and scheduling, which however requires centralized link scheduling. Under the primary interference model, link scheduling problem is equivalent to a maximum weighted hypergraph matching problem that is NP-complete. To solve the scheduling problem distributedly, locally greedy and randomized approximation algorithms are proposed and shown to have bounded worst-case performance. With random network coding, we obtain a fully distributed cross-layer design. Numerical results show promising throughput gain using the proposed algorithms, and surprisingly, in some cases even with less complexity than cross-layer design without broadcast advantage

QoSatAr: a cross-layer architecture for E2E QoS provisioning over DVB-S2 broadband satellite systems

This article presents QoSatAr, a cross-layer architecture developed to provide end-to-end quality of service (QoS) guarantees for Internet protocol (IP) traffic over the Digital Video Broadcasting-Second generation (DVB-S2) satellite systems. The architecture design is based on a cross-layer optimization between the physical layer and the network layer to provide QoS provisioning based on the bandwidth availability present in the DVB-S2 satellite channel. Our design is developed at the satellite-independent layers, being in compliance with the ETSI-BSM-QoS standards. The architecture is set up inside the gateway, it includes a Re-Queuing Mechanism (RQM) to enhance the goodput of the EF and AF traffic classes and an adaptive IP scheduler to guarantee the high-priority traffic classes taking into account the channel conditions affected by rain events. One of the most important aspect of the architecture design is that QoSatAr is able to guarantee the QoS requirements for specific traffic flows considering a single parameter: the bandwidth availability which is set at the physical layer (considering adaptive code and modulation adaptation) and sent to the network layer by means of a cross-layer optimization. The architecture has been evaluated using the NS-2 simulator. In this article, we present evaluation metrics, extensive simulations results and conclusions about the performance of the proposed QoSatAr when it is evaluated over a DVB-S2 satellite scenario. The key results show that the implementation of this architecture enables to keep control of the satellite system load while guaranteeing the QoS levels for the high-priority traffic classes even when bandwidth variations due to rain events are experienced. Moreover, using the RQM mechanism the user’s quality of experience is improved while keeping lower delay and jitter values for the high-priority traffic classes. In particular, the AF goodput is enhanced around 33% over the drop tail scheme (on average)

Cross-Layer Design of FDD-OFDM Systems based on ACK/NAK Feedbacks

It is well-known that cross-layer scheduling which adapts power, rate and user allocation can achieve significant gain on system capacity. However, conventional cross-layer designs all require channel state information at the base station (CSIT) which is difficult to obtain in practice. In this paper, we focus on cross-layer resource optimization based on ACK/NAK feedback flows in OFDM systems without explicit CSIT. While the problem can be modeled as Markov Decision Process (MDP), brute force approach by policy iteration or value iteration cannot lead to any viable solution. Thus, we derive a simple closed-form solution for the MDP cross-layer problem, which is asymptotically optimal for sufficiently small target packet error rate (PER). The proposed solution also has low complexity and is suitable for realtime implementation. It is also shown to achieve significant performance gain compared with systems that do not utilize the ACK/NAK feedbacks for cross-layer designs or cross-layer systems that utilize very unreliable CSIT for adaptation with mismatch in CSIT error statistics. Asymptotic analysis is also provided to obtain useful design insights

Cross-Layer Optimization of Fast Video Delivery in Cache-Enabled Relaying Networks

This paper investigates the cross-layer optimization of fast video delivery and caching for minimization of the overall video delivery time in a two-hop relaying network. The half-duplex relay nodes are equipped with both a cache and a buffer which facilitate joint scheduling of fetching and delivery to exploit the channel diversity for improving the overall delivery performance. The fast delivery control is formulated as a two-stage functional non-convex optimization problem. By exploiting the underlying convex and quasi-convex structures, the problem can be solved exactly and efficiently by the developed algorithm. Simulation results show that significant caching and buffering gains can be achieved with the proposed framework, which translates into a reduction of the overall video delivery time. Besides, a trade-off between caching and buffering gains is unveiled.Comment: 7 pages, 4 figures; accepted for presentation at IEEE Globecom, San Diego, CA, Dec. 201