The intense throughput and stringent delay requirements of Internet multimedia applications has spurred the need for new transport protocols with flexible transmission control. Current TCP congestion control adopts an Additive Increase Multiplicative Decrease (AIMD) algorithm that linearly increases or exponentially decreases the congestion window based on transmission acknowledgements. In this paper, we propose an AIMD-based media-aware congestion control that determines the optimal congestion window updating policy for multimedia transmission. The media-aware congestion control is formulated as a Partially Observable Markov Decision Process (POMDP), which maximizes the long-term expected quality of the received multimedia data. Moreover, we propose a reinforcement learning algorithm in order to estimate the environment and adapt to the source and network variations on the fly. Simulation results show that the proposed approach can significantly improve the received video quality, particularly at high source rates, compared to conventional TCP.

In this paper, we propose a quality-centric congestion control for multimedia streaming over wired IP networks, which we refer to as media-TCP-friendly congestion control (MTCC). Our solution adapts the sending rate to both the network condition and the application characteristics by explicitly considering the distortion impacts, delay deadlines, and interdependencies of different video packet classes. The media-aware solution is able to provide differential services for transmitting various packet classes and thereby, further improves the multimedia streaming quality compared to the conventional network-aware congestion control. We use finite-horizon Markov decision process (FHMDP) to determine the optimal congestion control policy that maximizes the long-term multimedia quality, while adhering to the horizon-$K$TCP-friendliness constraint, which ensures long-term fairness with existing TCP applications. Moreover, the proposed MTCC is able to achieve quality-based fairness among multimedia users. We derive sufficient conditions for multiple multimedia users to achieve quality-based fairness using MTCC congestion control. Note that the proposed solution only modifies the adaptation mechanism of the TCP congestion window size at the sender, without changing the design at the receiver side (i.e., each current TCP receiver can correctly receive and process MTCC streams). Our simulation results show that MTCC achieves more than 3 dB improvement in terms of PSNR over the conventional TCP congestion control approaches, with the largest improvements observed for real-time streaming applications requiring stringent playback delays.

In this paper, we propose a content-aware congestion control for multimedia streaming over TCP/IP networks. Unlike existing congestion control schemes over the Internet that adapt a user's sending rate merely to the network condition, our solution adapts the sending rate to both the network condition and the application characteristics by explicitly considering the distortion impacts, delay deadlines, and interdependencies of different multimedia packet classes. The proposed media-aware solution models the congestion control problem using a Foresighted-Packet-Based optimization and determines the optimal congestion control policy that maximizes the long-term multimedia quality, while adhering to the horizonK TCP-friendliness constraint, which ensures long-term fairness with existing TCP applications. Our simulation results show that the content-aware congestion control achieves more than 3dB improvement in terms of PSNR over the conventional TCP congestion control approaches, with the largest improvements observed for real-time streaming applications requiring stringent playback delays.

In this paper, we study how to optimize the transmission decisions of nodes aimed at supporting mission-critical applications, such as surveillance, security monitoring, and military operations, etc. We focus on a network scenario where multiple source nodes transmit simultaneously mission-critical data through relay nodes to one or multiple destinations in multi-hop wireless Mission-Critical Networks (MCN). In such a network, the wireless nodes can be modeled as agents that can acquire local information from their neighbors and, based on this available information, can make timely transmission decisions to minimize the end-to-end delays of the mission-critical applications. Importantly, the MCN needs to cope in practice with the time-varying network dynamics. Hence, the agents need to make transmission decisions by considering not only the current network status, but also how the network status evolves over time, and how this is influenced by the actions taken by the nodes. We formulate the agents' autonomic decision making problem as a Markov decision process (MDP) and construct a distributed MDP framework, which takes into consideration the informationally-decentralized nature of the multi-hop MCN. We further propose an online model-based reinforcement learning approach for agents to solve the distributed MDP at runtime, by modeling the network dynamics using priority queuing. We compare the proposed model-based reinforcement learning approach with other model-free reinforcement learning approaches in the MCN. The results show that the proposed model-based reinforcement learning approach for mission-critical applications not only outperforms myopic approaches without learning capability, but also outperforms conventional model-free reinforcement learning approaches.

Real-time multiuser multimedia applications, such as surveillance or monitoring using multiple cameras, have recently started to be deployed over flexible and low-cost multihop wireless networks. In such multimedia systems, the various sources (cameras) share the limited network resources and collaboratively forward the captured video streams to a remote central monitor. However, existing resource allocation schemes often ignore the dynamic application-layer video and network characteristics by focusing on the steady-state or worst-case operating conditions. This may result in inefficient allocation of the network resources. In this paper, we focus on determining whether the resource allocation for wireless video surveillance systems should be performed based on steady-state or worst-case operating conditions, or whether perpetual adaptation to the dynamically changing source and network conditions is desirable. We analyze three different types of solutions that have different information requirements: a centralized optimization approach, a decentralized game-theoretic approach (which guarantees a stable allocation), and a distributed greedy approach (which perpetually adapts allocation based on the local information exchanged among the neighboring nodes). We compare these three approaches using the following four metrics: 1) the total video quality; 2) the computational complexity; 3) the required control information overhead; and 4) the timely adaptation to the network and source variation. We show that in a static network, the game theoretic resource allocation is only better than the distributed greedy approach when the network transmission rates are high. In a dynamic network, the distributed greedy approach can outperform the other two approaches significantly in terms of video quality (peak signal-to-noise ratio). This shows that resource allocation solutions for multicamera wireless surveillance networks need to explicitly consider both the dynamic source characteristics and network conditions, rather than always relying on stable, but predetermined, allocations.

In this paper, we study distributed solutions for joint power-spectrum resource allocation among delay sensitive users over multi-carrier networks. Our work differs from prior research in two ways. First, unlike prior works that only consider the impact of effective throughput of a user, our work also considers the source traffic characteristics of applications and applies queuing analysis to quantify the packet transmission delay, which is especially important to a delay sensitive user. Secondly, most prior works focus on the equilibrium of the decentralized solutions that usually require global information in a static network. Instead, we focus on interactive adaptation methods for users to dynamically maximize their expected utility based on the local observed information. We propose an interactive learning framework for users to adapt their power/channel selection to the wireless networks with interference coupling among users. Our simulation results show that the proposed interactive learning approach with information exchange among users improves the total utility significantly.

Due to the dynamic nature of cognitive radio networks, multi-user video streaming (with various video traffic characteristics and QoS requirements) requires efficient dynamic channel selection schemes to exploit available spectrum resources. To do this, a wireless user needs to effectively model the dynamic wireless environment and estimate the delay of video packet transmission when selecting a specific frequency channel. In this paper, we apply the priority virtual queue model for these wireless users to adapt their channel selection and maximize video qualities. The simulation results show that the proposed channel selection solution based on priority scheduling outperforms the conventional dynamic channel selection scheme by 2 dB (PSNR).

In this paper, we propose a dynamic channel-selection solution for autonomous wireless users transmitting delay-sensitive multimedia applications over cognitive radio networks. Unlike prior works that seldom consider the requirement of the application layer, our solution explicitly considers various rate requirements and delay deadlines of heterogeneous multimedia users. Note that the users usually possess private utility functions, application requirements, and distinct channel conditions in different frequency channels. To efficiently manage available spectrum resources in a decentralized manner, information exchange among users is necessary. Hence, we propose a novel priority virtual queue interface that determines the required information exchanges and evaluates the expected delays experienced by various priority traffics. Such expected delays are important for multimedia users due to their delay-sensitivity nature. Based on the exchanged information, the interface evaluates the expected delays using priority queuing analysis that considers the wireless environment, traffic characteristics, and the competing users' behaviors in the same frequency channel. We propose a dynamic strategy learning (DSL) algorithm deployed at each user that exploits the expected delay and dynamically adapts the channel selection strategies to maximize the user's utility function. We simulate multiple video users sharing the cognitive radio network and show that our proposed solution significantly reduces the packet loss rate and outperforms the conventional single-channel dynamic resource allocation by almost 2 dB in terms of video quality.

Emerging multi-hop wireless networks provide a low-cost and flexible infrastructure that can be simultaneously utilized by multiple users for a variety of applications, including delay-sensitive multimedia transmission. However, this wireless infrastructure is often unreliable and provides dynamically varying resources with only limited quality of service (QoS) support for multimedia applications. To cope with the time-varying QoS, existing algorithms often rely on non-scalable, flow-based optimizations to allocate the available network resources (paths and transmission opportunities) across the various multimedia users. Moreover, previous research seldom optimizes jointly the dynamic routing with the adaptation and protection techniques available at the medium access control (MAC) or physical (PHY) layers. In this paper, we propose a distributed packet-based cross-layer algorithm to maximize the decoded video quality of multiple users engaged in simultaneous real-time streaming sessions over the same multi-hop wireless network. Our algorithm explicitly considers packet-based distortion impact and delay constraints in assigning priorities to the various packets and then relies on priority queuing to drive the optimization of the various users' transmission strategies across the protocol layers as well as across the multi-hop network. The proposed solution is enabled by the scalable coding of the video content (i.e. users can transmit and consume video at different quality levels) and the cross-layer optimization strategies, which allow priority-based adaptation to varying channel conditions and available resources. The cross-layer strategies - application layer packet scheduling, the policy for choosing the relays, the MAC retransmission strategies, the PHY modulation and coding schemes - are optimized per packet, at each node, in a distributed manner. The main component of the proposed solution is a low-complexity, distributed, and dynamic routing algorithm, which relies on prioritized queuing to select the path and time reservation for the various packets, while explicitly considering instantaneous channel conditions, queuing delays and the resulting interference. Our results demonstrate the merits and need for end-to-end cross-layer optimization in order to provide an efficient solution for real-time video transmission using existing protocols and infrastructures. Importantly, our proposed delay-driven, packet-based transmission is superior in terms of both network scalability and video quality performance to previous flow-based solutions that statically allocate resources based on predetermined paths and rate requirements. In addition, the results provide important insights that can guide the design of network infrastructures and streaming protocols for video streaming.

Various packet scheduling, dynamic routing, error-protection, and channel adaptation strategies have been proposed at different layers of the protocol stack to address multi-user video streaming over multihop wireless networks. However, these cross-layer transmission strategies can be efficiently optimized only if they use accurate information about the network conditions and hence, are able to timely adapt to network changes. Due to the informationally decentralized nature of the multihop wireless network, performing centralized optimization for delay-sensitive video streaming application based on global information about the network status is not practical. Distributed solutions that adapt the transmission strategies based on timely information feedback need to be considered. To acquire this information feedback for cross-layer adaptation, we deploy an overlay infrastructure, which is able to relay the necessary information about the network status and incurred delays across different network “horizons” (i.e., across a different number of hops in a predetermined period of time). In this paper, we propose a distributed streaming approach that is optimized based on the local information feedback acquired from the various network horizons. We investigate the distributed cross-layer adaptation at each wireless node by considering the advantages resulting from an accurate and frequent network information feedback from larger horizons as well as the drawbacks resulting from an increased transmission overhead. Based on the information feedback, we can estimate theriskthat packets from different priority classes will not arrive at their destination before their decoding deadline expires. Subsequently, the various transmission strategies such as packet scheduling, retransmission limit and dynamic routing policies are adapted to jointly consider the estimated risk as well as the impact in terms of distortion

We design a complete baseband system of a high data rate packet-type OFDM receiver. For mobility support and channel-gain phase tracking, we propose a simple pilot tone insertion method that not only facilitates low-complexity channel gain tracking but also enables carrier frequency tracking. Besides, an efficient mechanism for packet boundary detection is also provided. In addition to channel gain tracking and packet boundary detection, timing synchronization, coarse frequency offset estimation, and fine frequency offset tracking issues are also discussed and simulated.