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In this letter, we consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing their content. In the considered P2P system, autonomous and self-interested peers use a Markov Decision Process (MDP) framework to determine their upload bandwidth allocations, which maximize their individual utilities. This framework enables the peers to make foresighted decisions on their bandwidth allocations, by considering the future impact of their decisions. In this letter, we focus on the impact of the peers’ bounded rationality on their resource reciprocation strategies and ultimately, on their achievable utilities. Specifically, we consider peers who have only a limited ability to model the other peers' strategies for resource reciprocation, and study how this impacts their own decisions.

We propose a novel cross layer solution for optimizing the performance of multiple users, which are concurrently transmitting their delay-sensitive video streams over the same shared contention-based wireless LAN. To improve the performance of their applications, they dynamically adapt their medium access control (MAC) strategies (i.e., retry limits). Higher retry limits will benefit the user, but they will also lead to an increased congestion for the other users. Hence, we explicitly consider how the cross layer strategy adopted by one video user will impact the strategies of the competing users. Specifically, we analytically model how the cross layer transmission strategies adopted by each wireless user impact their own incurred distortion and delay but also that of their competing wireless stations. We also propose a distributed, low-complexity, and highly scalable optimization algorithm which can maximize the utility of the delay-sensitive video application. We compare our scheme to existing retry limit-based solutions, and the simulation results show that the proposed solution can outperform these solutions and maximize the users' utilities in a highly congested multiuser wireless environment.

In this letter, we consider a simple two-user channel access game and investigate how to improve the performance of Nash equilibrium (NE) by employing a correlated device that coordinates the users' actions and leads them to play a higher efficiency correlated equilibrium (CE). Unlike existing papers, we discuss both the public and private CEs and quantify their performances in various simple, but illustrative scenarios. Moreover, we propose a simple procedure for the correlated device to select the CE leading to the highest payoff, without the need for the users to report their private utility functions.

Stream mining applications require the identification of several different attributes in data content and hence rely on a distributed set of cascaded statistical classifiers to filter and process the data dynamically. In this letter, we introduce a novel methodology for configuring cascaded classifier topologies, specifically binary classifier trees, with optimized operating points after jointly considering the misclassification cost of each end-to-end class of interest in the tree, the resource constraints for every classifier, and the confidence level of each data object that is classified. By configuring multiple operating points per classifier, we enable not only intelligent load shedding when resources are scarce but also intelligent replication of low confidence data across multiple edges when excess resources are available. Using a classifier tree constructed from support vector machine-based sports image classifiers, we verify huge cost savings and discuss how different classifier placements and costs can influence the gains obtained by various algorithms.

The objective of joint source channel coding (JSCC) is to optimally allocate the available channel bandwidth between source coding and channel coding in order to minimize the average distortion in the presence of packet losses. Unlike previous research, which focuses on determining distortion-optimized methods for JSCC using either assumptions about the rate-distortion (R-D) curve properties of source data (e.g., convexity) or experimental R-D data, we explicitly employ the analytical operational R-D model of a three-dimensional wavelet video coder to solve the JSCC problem. Using this model, we formulated the JSCC optimization as a nonlinear programming (NP) problem. By expressing the NP problem in terms of a dynamic programming equation, we derived the necessary conditions that facilitate the search for global optimal solutions. In this letter, we focus on finding the optimal JSCC problem for subband video coding and not on deriving computationally efficient methods for JSCC.

Motion-compensated temporal filtering (MCTF) is an essential ingredient of the recently developed wavelet-based scalable video coding schemes. Lifting implementation of these decompositions represents a versatile tool for spatiotemporal optimizations, and several improvements have already been proposed in this framework. In this paper, we propose new spatiotemporal update operators based either on weighted averages of multiple connected pixels or on nonlinear filtering of these pixels. We demonstrate the improved performance of the proposed operators, both theoretically and experimentally.