Hyunggon Park

Bargaining Strategies for Networked Multimedia Resource Management

Multiuser multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service (QoS) required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider two bargaining solutions for our resource management problem: the Nash bargaining solution (NBS) and the Kalai-Smorodinsky bargaining solution (KSBS). We provide interpretations for the two investigated bargaining solutions for multiuser resource allocation: the NBS can be used to maximize the system utility, while the KSBS ensures that all users incur the same utility penalty relative to the maximum achievable utility. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation. We show that the bargaining solutions exhibit important properties (axioms) that can be used for effective multimedia resource allocation. Moreover, we propose several criteria for determining bargaining powers for these solutions, which enable us to provide additional flexibility in choosing solution by taking into consideration the visual quality impact, the deployed spatiotemporal resolutions, etc. We also determine the complexity of these solutions for our application and quantify the performance of the proposed bargaining-based resource strategies for different scenarios.

Video streaming over P2P networks: Challenges and opportunities

A robust real-time video communication service over the Internet in a distributed manner is an important challenge, as it influences not only the current Internet structure but also the future Internet evolution. In this context, Peer-to-Peer (P2P) networks are playing an imperative position for providing efficient video transmission over the Internet. Recently, several P2P video transmission systems have been proposed for live video streaming services or video-on-demand services over the Internet. In this paper, we describe and discuss existing video streaming systems over P2P. Efficient (delay tolerant and intolerant) data sharing mechanisms in P2P and current video coding trends are elaborated in detail. Moreover, video streaming solutions (live and on-demand) over P2P from the perspective of tree-based and mesh-based systems are explained. Finally, the conclusion is drawn with key challenges and open issues related to video streaming over P2P.

ECG Authentication System Design Based on Signal Analysis in Mobile and Wearable Devices

We propose a practical system design for biometrics authentication based on electrocardiogram (ECG) signals collected from mobile or wearable devices. The ECG signals from such devices can be corrupted by noise as a result of movement, signal acquisition type, etc. This leads to a tradeoff between captured signal quality and ease of use. We propose the use of cross correlation of the templates extracted during the registration and authentication stages. The proposed approach can reduce the time required to achieve the target false acceptance rate (FAR) and false rejection rate (FRR). The proposed algorithms are implemented in a wearable watch for verification of feasibility. In the experiment results, the FAR and FRR are 5.2% and 1.9%, respectively, at approximately 3 s of authentication and 30 s of registration.

Fairness Strategies for Wireless Resource Allocation Among Autonomous Multimedia Users

Recent research in wireless multimedia streaming has focused on optimizing the multimedia quality in isolation, at each station. However, the cross-layer transmission strategy deployed at one station impacts and is impacted by the other stations, as the wireless network resource is shared among all competing users. Hence, efficient and fair resource management for autonomous wireless multimedia users becomes very important. We consider quality-based fairness schemes based on axiomatic bargaining theory, which can ensure that the autonomous multimedia stations incur the same drop in multimedia quality as compared to a maximum achievable quality for each wireless station. Implementing this quality-based fairness solution in the time-varying channel condition requires high-computational complexity and communication overheads. Hence, we develop solutions that significantly reduce the computational complexity and communication overheads. Our simulations show that the proposed game-theoretic resource management can indeed guarantee desired utility-fair allocations when wireless stations deploy different cross-layer strategies.

Evolution of Resource Reciprocation Strategies in P2P Networks

In this paper, we consider the resource reciprocation among self-interested peers in peer-to-peer (P2P) networks, which is modeled as a stochastic game. Peers play the game by determining their optimal strategies for resource distributions using a Markov decision process (MDP) framework. The optimal strategies enable the peers to maximize their long-term utility. Unlike in conventional MDP frameworks, we consider heterogeneous peers that have different and limited ability to characterize their resource reciprocation with other peers. This is due to the large complexity requirements associated with their decision making processes. We analytically investigate these tradeoffs and show how to determine the optimal number of state descriptions, which maximizes each peers average cumulative download rates given a limited time for computing the optimal strategies. We also investigate how the resource reciprocation evolves over time as peers adapt their reciprocation strategies by changing the number of state descriptions. Then, we study how resulting download rates affect their performance as well as that of the other peers with which they interact. Our simulation results quantify the tradeoffs between the number of state descriptions and the resulting utility. We also show that evolving resource reciprocation can improve the performance of peers which are simultaneously refining their state descriptions.

Approximate decoding approaches for network coded correlated data

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

Multi-User Multimedia Resource Management using Nash Bargaining Solution

Multi-user multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider the Nash bargaining solution (NBS) for our resource management problem. We provide interpretations for the NBS for multi-user resource allocation: the NBS can be used to maximize the system quality. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation.

Congestion game modeling for brokerage based multimedia resource management

We introduce the concept of resource brokers, which enables efficient and fair management of the available network resources for multimedia users in large networks while reducing the complexity of a central resource allocation authority. To manage the available resources, the resource brokers deploy axiomatic bargaining solutions from economic game theory in order to explicitly consider the utility impact for different resource allocation schemes. We focus on the Kalai-Smorodinsky bargaining solution because it can successfully model relevant autonomous utility-aware fairness policies for multimedia users. Based on the interpretations of the bargaining solutions, we can model the proposed resource allocation scheme as a utility-driven congestion game, thereby ensuring that the presented resource management game will reach a steady-state after a finite (small) number of changes across resource brokers (i.e., at least one Nash equilibrium exists).

An improved approximate decoding with correlated sources

We consider ad hoc sensor network topologies that aim for distributed delivery of correlated delay-sensitive data. In order for efficient data delivery, network coding technique in conjunction with approximate decoding algorithm is deployed. The approximate decoding algorithm enables receivers to recover the original source data even when the number of received data packets is not sufficient for decoding. Therefore, it leads to significantly improved decoding performance and enhanced robustness for delay-sensitive data. In this paper, we further improve the approximate decoding algorithm by explicitly considering the characteristics of the correlation. Specifically, we study the case where the source data are correlated by a simple linear correlation, which is quantified by a similarity factor. We investigate several properties of the proposed algorithm and analyze the impact of the similarity factor on the decoding performance. Our experimental results confirm the properties of the proposed approximate decoding algorithm with linear correlation.

Approximate decoding for network coded inter-dependent data

In this paper, we consider decoding of loss tolerant data encoded by network coding and transmitted over error-prone networks. Intermediate network nodes typically perform the random linear network coding in a Galois field and a Gaussian elimination is used for decoding process in the terminal nodes. In such settings, conventional decoding approaches can unfortunately not reconstruct any encoded data unless they receive at least as many coded packets as the original number of packets. In this paper, we rather propose to exploit the incomplete data at a receiver without major modifications to the conventional decoding architecture. We study the problem of approximate decoding for inter-dependent sources where the difference between source vectors is characterized by a unimodal distribution. We propose a mode-based algorithm for approximate decoding, where the mode of the source data distribution is used to reconstruct source data. We further improve the mode-based approximate decoding algorithm by using additional short information that is referred to as position similarity information (PSI). We analytically study the impact of PSI size on the approximate decoding performance and show that the optimal size of PSI can be determined based on performance requirements of applications. The proposed approach has been tested in an illustrative example of data collection in sensor networks. The simulation results confirm the benefits of approximate decoding for inter-dependent sources and further show that 93.3% of decoding errors are eliminated when the approximate decoding uses appropriate PSI.