The scalability of multi-user SLAM is fundamentally limited by the constrained network and computational resources. Existing approaches either focus on SLAM for single-user scenarios or overload networks and servers by streaming dense, uniform camera data and treating all users equally. This results in poor pose estimation accuracy or slow updates to multiple users. Our key insight is that the sparsity and heterogeneity of user activity reveal that not all users or frames contribute equally to the shared map. Building on this, we propose SPARC - Proximity-aware Scheduling of AR Mapping and a blur-aware adaptive Cloud-based GenAI sampling method, which together form a cloud-native framework for efficient multi-user SLAM. On the client side, adaptive, context-aware frame transmission selectively forwards high-value frames. On the server side, generative AI (GenAI)-based upsampling reconstructs dense scene features from sparse inputs, while a proximity-aware scheduler prioritizes updates for users with higher drift or critical interactions. Together, these components reduce redundant transmission, improve resource allocation, and enable fairness without sacrificing accuracy. We show through extensive experimentation that our method reduces the latency by 2× to 4× compared to state-of-the-art while maintaining similar or better tracking accuracy. More broadly, this work reimagines SLAM as a cloud-native service, paving the way for scalable, real-time AR/VR applications where many users seamlessly interact in shared environments.

This demo introduces WaveBox, a dynamic, software-defined waveform generation system developed to assess the resilience of communication networks against many types of interference scenarios. WaveBox features seamless waveform switching, allowing users to efficiently adjust interference patterns to adapt to diverse operational scenarios. We will showcase the system's effectiveness and versatility, highlighting its ability to adapt to evolving mission requirements. Additionally, the system's intuitive graphical user interface (GUI) supports rapid waveform adjustments, enhancing its responsiveness in dynamic environments. WaveBox can provide a flexible software-defined tool for evaluating the robustness of wireless systems.

Ensuring the safety and reliability of emerging Advanced Aerial Mobility (AAM) systems requires wireless communication to provide real-time monitoring and control information to ground stations. This greatly depends on the quality of the wireless links during aerial transit. In this demonstration, we present AirTwinX, designed to emulate flight control of flying vehicles while generating high-fidelity, context-aware models for air-to-air (AA) and air-to-ground (AG) communication links. Using GPU-accelerated ray tracing, AirTwinX can predict the quality of wireless links with near real-time updates based on the environmental geometry observed during flight. This data is then used to guide autonomous control decision-making. Additionally, the vehicle control toolchain employed in this work is based on software-in-the-loop (SITL) emulation of a commercial flight controller, enabling seamless translation of control policies from simulation to real-world hardware.

In highly dynamic and contested RF environments, communication systems must swiftly adapt to fluctuating spectral conditions while ensuring network quality of service (QoS). Maintaining link synchronization and spectral efficiency during waveform adaptation is particularly challenging due to the high mobility and autonomy of devices, coupled with the possibility of operating in GPS-denied environments. In this demo, we introduce a scalable, high-speed FPGA-based parallel decoding algorithm that leverages the HORNets signature adaptation protocol to address these challenges. Our solution preserves link synchronization within a multi-node network and enables efficient, lightweight waveform adaptation without reliance on GPS. The algorithm's resilience and effectiveness are demonstrated using a three-node cluster configuration, all subjected to non-colored or colored intentional interference.

We propose ASL360, an adaptive deep reinforcement learning-based scheduler for on-demand 360° video streaming to mobile VR users in next generation wireless networks. We aim to maximize the overall Quality of Experience (QoE) of the users served over a UAV-assisted 5G wireless network. Our system model comprises a macro base station (MBS) and a UAV-mounted base station which both deploy mm-Wave transmission to the users. The 360°video is encoded into dependent layers and segmented tiles, allowing a user to schedule downloads of each layer’s segments. Furthermore, each user utilizes multiple buffers to store the corresponding video layer’s segments. We model the scheduling decision as a Constrained Markov Decision Process (CMDP), where the agent selects Base or Enhancement layers to maximize the QoE and use a policy gradient-based method (PPO) to find the optimal policy. Additionally, we implement a dynamic adjustment mechanism for cost components, allowing the system to adaptively balance and prioritize the video quality, buffer occupancy, and quality change based on real-time network and streaming session conditions. We demonstrate that ASL360 significantly improves the QoE, achieving approximately 2 dB higher average video quality, 80% lower average rebuffering time, and 57% lower video quality variation, relative to competitive baseline methods. Our results show the effectiveness of our layered and adaptive approach in enhancing the QoE in immersive video streaming applications, particularly in dynamic and challenging network environments.

Delay-sensitive Internet of Things (IoT) applications continue to grow in prevalence as new wireless technologies are adopted. Since these applications often operate in unknown dynamic environments, reinforcement learning (RL) has emerged as an effective method to learn optimal decision policies that improve their overall performance. However, typical data-driven RL techniques that have been adopted to solve these problems do not exploit available knowledge of system dynamics. Consequently, they must “learn” some information about the system that may already be known to the system's designer. Post-decision state (PDS) learning, on the other hand, leverages known system information (i.e., it is “physics-informed”) to simplify the learning task and improve learning performance. However, this comes at the cost of increased computational complexity, and makes it impractical to implement on resource constrained devices. This work introduces stochastic PDS learning, a novel RL algorithm that combines traditional PDS learning with stochastic sampling to produce a physics-informed RL agent that can leverage known system information even with limited computational resources. Performance of stochastic PDS learning is compared against numerous traditional RL algorithms in the context of a delaysensitive energy-efficient scheduling problem simulated as an environment in Gymnasium.

Efficient power use and accurate viewport information are key factors in enabling effective aerial 360° video delivery to virtual reality (VR) clients for emerging remote immersion societal applications. We explore a learning-based framework for transmission power allocation and robust viewport identification in UAV-based 360° video streaming to a ground user/VR client that aims to maximize the delivered viewport quality and minimize the video playback stall time on the user’s VR headset. We model the problem of interest as a Markov decision process (MDP) encompassing the UAV’s transmit power, the VR client’s video playback stall time, and the full-identification outage of the user’s observed viewport in the MDP reward function. Our framework integrates an effective scalable 360° video tiling representation of the captured content that ensures for the client (i) maximum delivered viewport quality given the available UAV transmission rate and (ii) VR application robustness to partial viewport outages, at the same time. We formulate a novel learning-based method for adaptive transmission power allocation and predicted viewport enlargement, to solve the problem of interest. Relative to multiple reference methods, we demonstrate through experiments that our framework can achieve up to 8dB improvement in viewport PSNR and an 85% reduction in full-viewport identification outage, while using 60% less transmit power and experiencing negligible video stall times.

In this work, we present a new federation framework for Union-Labs, an innovative cloud-based resource-sharing infrastructure designed for next-generation (NextG) and Internet of Things (IoT) over-the-air (OTA) experiments. The framework aims to reduce the federation complexity for testbeds developers by automating tedious backend operations, thereby providing scalable federation and remote access to various wireless testbeds. We first describe the key components of the new federation framework, including the Systems Manager Integration Engine (SMIE), the Automated Script Generator (ASG), and the Database Context Manager (DCM). We then prototype and deploy the new Federation Plane on the Amazon Web Services (AWS) public cloud, demonstrating its effectiveness by federating two wireless testbeds: i) UB NeXT, a 5G-and-beyond (5G+) testbed at the University at Buffalo, and ii) UT IoT, an IoT testbed at the University of Utah1.

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is discontinuous reception (DRX). DRX allows a device’s radio frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this article, we model DRX as a Markov decision process (MDP). This MDP is solved using a low-complexity “DRX-aware” value iteration algorithm, then verified through simulation and analytical analysis. Further, the energy-latency tradeoff is explored by varying the device’s priority on either energy or latency in addition to varying the traffic intensity. Finally, a method of traffic estimation is applied, and the model’s performance in an environment with time-varying traffic intensity is explored. This approach is compared with a reinforcement learning approach, showing that the traffic estimation approach is better suited to the problem of DRX optimization.

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is Discontinuous Reception (DRX). DRX allows a device's Radio Frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this paper, we model DRX as a Markov Decision Process (MDP). This MDP is solved using a dynamic programming approach and verified through simulation. Further, the energy-latency tradeoff is explored by varying the device's priority on either energy or network performance in addition to varying the traffic intensity.

The National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) is the latest passive remote sensing satellite operating in the protected L-band spectrum from 1.400 to 1.427 GHz. SMAP provides global-scale soil moisture images with point-wise passive scanning of the earth’s thermal radiations. SMAP takes multiple samples in frequency and time from each antenna footprint to increase the likelihood of capturing RFI-free samples. SMAP’s current RFI detection and mitigation algorithm excludes samples detected to be RFI-contaminated and averages the remaining samples. But this approach can be less effective for harsh RFI environments, where RFI contamination is present in all or a large number of samples. In this paper, we investigate a bias-free weighted sum of samples estimator, where the weights can be computed based on the RFI’s statistical properties.

The coexistence of a wide variety of different applications with diverse Quality of Service (QoS) requirements calls for more sophisticated radio resource scheduling (RRS) in 5G networks compared to previous generations. To address this challenge, a growing body of research formulates the RRS problem as a Markov decision process (MDP) and aims to solve it using deep reinforcement learning (DRL). A key consideration when formulating an MDP is the choice of reward function, which determines the goal of the decision agent. Despite the reward function being a critical component of an MDP, there is currently no systematic study comparing how different reward functions affect network performance. To this end, we carry out a comparative study of the delay and overflow performance using several reward functions that aim to minimize packet delays. Through extensive simulations under different traffic and channel conditions, we identify a reward function that can achieve near optimal delay with up to 55 − 67% fewer packet drops than the other investigated options, and does not require any tuning.

The coexistence of a wide variety of different applications with diverse Quality of Service (QoS) and Quality of Experience (QoE) requirements calls for more sophisticated radio resource scheduling in 5G and beyond (5GB) networks compared to previous generations. To address this challenge, a growing body of research has explored deep reinforcement learning (DRL) to solve the radio resource scheduling problem. In this paper, we review representative literature on the topic of downlink scheduling for 5GB networks using DRL, with emphasis on fine-grained approaches that directly allocate resource blocks (RBs) to user equipments (UEs). We conclude by discussing four ways to improve upon this early-stage research and identify some open problems that must be solved to make DRL a viable solution to the downlink scheduling problem in 5GB networks.

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper, we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used to characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively kept below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.

In this article we explore new techniques that can enable open remote experimentation for mobile networks. We first propose a cloud-based framework called CloudRAFT, based on which experimenters are allowed to remotely access and control experimental resources via public cloud AWS and share the resulting data and code via the cloud. Then, we discuss the enabling techniques for CloudRAFT, including Amazon serverless service, VNC-based remote command line, and Websocket-based real time communications, among others. Finally, we showcase the application of these techniques in enabling remote access to UB NeXT, a software-defined testbed that has been developed at University at Buffalo for wireless mobile network modeling, optimization and deployment. This work verifies the feasibility of accessing, controlling and sharing wireless testbeds through a remote public cloud.

Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

We introduce RF-SITL, a radio frequency (RF) software-in-the-loop (SITL) channel emulator developed with GNU Radio and the University at Buffalo’s Airborne Networking and Communications (UB-ANC) emulator to enable integrated simulation of systems comprising multiple unmanned aerial vehicles (UAVs) interacting over a wireless communication channel. RF-SITL could be paired with any multi-robot simulator to enable I/Q sample-level fidelity simulation of communication interactions between the robots by accurately simulating channel effects, including interference, noise, distance-dependent path loss, and packet losses. RF-SITL works as follows: 1) it instantiates a virtual software-defined transceiver in GNU Radio for each UAV simulated in the UB-ANC Emulator; 2) it builds an interference channel model in which each network node receives the superposition of signals transmitted from other nodes; and 3) it synchronizes the location of each simulated UAV in the UB-ANC Emulator with the virtualized RF transceivers in RF-SITL, such that the communication channel between nodes can accurately model distance-dependent channel effects, such as path loss. With these capabilities, we can use both off-the-shelf and custom-built signal processing flowgraphs that simulate Gaussian Minimum Shift Keying (GMSK), 802.11-like Orthogonal Frequency Division Multiplexing (OFDM), and direct sequence spread-spectrum (DSSS) links in GNU Radio to simulate swarm UAV networks prior to their deployment in software-defined radios in a swarm UAV network.

Reinforcement learning (RL) is increasingly being used to optimize resource-constrained wireless Internet of Things (IoT) devices. However, existing RL algorithms that are lightweight enough to be implemented on these devices, such as$Q$-learning, converge too slowly to effectively adapt to the experienced information source and channel dynamics, while deep RL algorithms are too complex to be implemented on these devices. By integrating basic models of the IoT system into the learning process, the so-called postdecision state (PDS)-based RL can achieve faster convergence speeds than these alternative approaches at lower complexity than deep RL; however, its complexity may still hinder the real-time and energy-efficient operations on IoT devices. In this article, we develop efficient hardware accelerators for PDS-based RL. We first develop an arithmetic hardware acceleration architecture and then propose a stochastic computing (SC)-based reconfigurable hardware architecture. By using simple bitwise computations enabled by SC, we eliminate costly multiplications involved in PDS learning, which simultaneously reduces the hardware area and power consumption. We show that the computational efficiency can be further improved by using extremely short stochastic representations without sacrificing learning performance. We demonstrate our proposed approach on a simulated wireless IoT sensor that must transmit delay-sensitive data over a fading channel while minimizing its energy consumption. Our experimental results show that our arithmetic accelerator is$5.3\times $faster than$Q$-learning and$2.6\times $faster than a baseline hardware architecture, while the proposed SC-based architecture further reduces the critical path of the arithmetic accelerator by 87.9%.

Recent growth in the Internet of Things (IoT) has been remarkable. Among the solutions to accommodate such a growth is Cellular IoT (C-IoT), comprising a group of technologies extended from legacy cellular infrastructures. One of the key goals of C-IoT technologies is to extend the battery life of UEs (User Equipment) in the network. However, this often comes at the cost of degrading network performance. This work attempts to identify, categorize, and analyze the available literature on this problem. The literature is broadly categorized into three sections: scheduling, data processing, and sleep modes. In each of these sections, the literature is further sub categorized. Finally, a direction for future research is identified and discussed.

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.

We investigate a novel communications system that integrates scalable multi-layer 360° video tiling, viewport-adaptive rate-distortion optimal resource allocation, and VR-centric edge computing and caching, to enable future high-quality untethered VR streaming. Our system comprises a collection of 5G small cells that can pool their communication, computing, and storage resources to collectively deliver scalable 360° video content to mobile VR clients at much higher quality. Our major contributions are rigorous design of multi-layer 360° tiling and related models of statistical user navigation, and analysis and optimization of edge-based multi-user VR streaming that integrates viewport adaptation and server cooperation. We also explore the possibility of network coded data operation and its implications for the analysis, optimization, and system performance we pursue here. We demonstrate considerable gains in delivered immersion fidelity, featuring much higher 360° viewport peak signal to noise ratio (PSNR) and VR video frame rates and spatial resolutions.

We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications. We aim to find a scheduling policy that minimizes the queuing delay experienced by the users. We formulate this problem as a Markov Decision Process (MDP) that integrates the channel quality indicator (CQI) of each user in each RB, and queue status of each user. To solve this complex problem involving high dimensional state and action spaces, we propose a Deep Reinforcement Learning based scheduling framework that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm to minimize the queuing delay experienced by the users. Our extensive experiments demonstrate that our approach outperforms state-of-the-art benchmarks in terms of average throughput, queuing delay, and fairness, achieving up to 55% lower queuing delay than the best benchmark.

We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel. We aim to find the optimal transmission scheduling policy that minimizes the packet queuing delay given the available harvested energy. We formulate the problem as a Markov decision process (MDP) over a state-space spanned by the transmitter's buffer, battery, and channel states, and analyze the structural properties of the resulting optimal value function, which quantifies the long-run performance of the optimal scheduling policy. We show that the optimal value function (i) is non-decreasing and has increasing differences in the queue backlog; (ii) is non-increasing and has increasing differences in the battery state; and (iii) is submodular in the buffer and battery states. Taking advantage of these structural properties, we derive an approximate value iteration algorithm that provides a controllable tradeoff between approximation accuracy, computational complexity, and memory, and we prove that it converges to a near-optimal value function and policy. Our numerical results confirm these properties and demonstrate that the resulting scheduling policies outperform a greedy policy in terms of queuing delay, buffer overflows, energy efficiency, and sensor outages.

Advances in hardware, software and sensing are bringing swarms of robots to daily life. A major challenge in enabling such applications is multi-robot coordination. Most multi-robot coordination algorithms are developed under the assumption of perfect communication, which does not hold in practical wireless networks. To understand the consequences of this, we investigate the performance of a representative task allocation algorithm for multi-robot systems, namely, the Asynchronous Consensus Based Bundle Algorithm (ACBBA), in realistic network conditions. We show that the ACBBA deviates from its desired theoretical behavior when deployed in a realistic network. This manifests in the form of redundant task assignments across agents, which violates the algorithm's "conflict-free" assignment constraint and degrades the task allocation efficiency. We explore several network-based mitigations to this problem.

We study downlink transmission in a multi-band heterogeneous network comprising unmanned aerial vehicle (UAV) small base stations and ground-based dual mode mmWave small cells within the coverage area of a microwave (μW) macro base station. We formulate a two-layer optimization framework to simultaneously find efficient coverage radius for the UAVs and energy efficient radio resource management for the network, subject to minimum quality-of-service (QoS) and maximum transmission power constraints. The outer layer derives an optimal coverage radius/height for each UAV as a function of the maximum allowed path loss. The inner layer formulates an optimization problem to maximize the system energy efficiency (EE), defined as the ratio between the aggregate user data rate delivered by the system and its aggregate energy consumption (downlink transmission and circuit power). We demonstrate that at certain values of the target SINR τ introducing the UAV base stations doubles the EE. We also show that an increase in τ beyond an optimal EE point decreases the EE.

We consider an energy harvesting sensor transmit- ting latency-sensitive data over a fading channel. We aim to find the optimal transmission scheduling policy that minimizes the packet queuing delay given the available harvested energy. We formulate the problem as a Markov decision process (MDP) over a state-space spanned by the transmitter's buffer, battery, and channel states, and analyze the structural properties of the resulting optimal value function, which quantifies the long-run performance of the optimal scheduling policy. We show that the optimal value function (i) is non- decreasing and has increasing differences in the queue backlog; (ii) is non-increasing and has increasing differences in the battery state; and (iii) is submodular in the buffer and battery states. Our numerical results confirm these properties and demonstrate that the optimal scheduling policy outperforms a so-called greedy policy in terms of sensor outages, buffer overflows, energy efficiency, and queuing delay.

Two-hop relay transmissions can be exploited to improve the coverage and spectral efficiency of cellular networks. Using stochastic geometry, we develop analytical models for the uplink coverage probability and spectral efficiency of two-hop device-to-device (D2D) relay-assisted cellular networks, in which user equipments (UEs) with poor direct links to the base station (BS) can complete their transmissions with the help of a relay UE. We assume overlay inband D2D operation such that D2D link transmissions (from the source UE to the relay UE) use uplink spectrum, but do not interfere with uplink transmissions in the same cell. We model the base stations (BSs) and candidate relay UEs as Poisson point processes (PPPs) and derive the two-hop coverage probability capturing the correlation of the two links in two-hop transmission. We then model the uplink spectral efficiency, which takes into account the D2D link resource usage and depends on the mode selection strategy. Numerical results show that the analytical models provide reasonable approximations of the cellular uplink performance with a Rayleigh fading assumption. They also reveal insights into the coverage and spectral efficiency gains achievable when leveraging D2D relays on the uplink of a cellular network.

The susceptibility of millimeter waveform propagation to blockages limits the coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we derive expressions for the downlink coverage probability of relay-assisted mmWave cellular networks when the D2D links are implemented in either uplink mmWave or uplink microwave bands. We further investigate the spectral efficiency (SE) improvement in the cellular downlink, and the effect of D2D transmissions on the cellular uplink. For mmWave links, we derive the coverage probability using dominant interferer analysis while accounting for both blockages and beamforming gains. For microwave D2D links, we derive the coverage probability considering both line-of-sight and non-line-of-sight (NLOS) propagation. Numerical results show that downlink coverage and SE can be improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve better coverage because D2D connections can be established under NLOS conditions. However, mmWave D2D relays achieve better coverage when the density of interferers is large because blockages eliminate interference from NLOS interferers. The SE on the downlink depends on the relay mode selection strategy, and mmWave D2D relays use a significantly smaller fraction of uplink resources than microwave D2D relays.

This paper presents the design of a novel variable stiffness gripper with two parallel fingers (jaws). Compliance of the system is generated by using permanent magnets as the nonlinear springs. Based on the presented design, the position and stiffness level of the fingers can be adjusted simultaneously by changing the air gap between the magnets. The modeling of magnetic repulsion force and stiffness are presented and verified experimentally. An experiment is also conducted to demonstrate the functionality of the gripper to improve safety when a fragile object was grasped and the gripper collided with an obstacle.

Advancements in the design of drones have led to their use in varied environments and applications such as battle field surveillance. In such scenarios, swarms of drones can coordinate to survey a given area. We consider the problem of covering an arbitrary area containing obstacles using multiple drones, i.e., the so-called coverage path planning (CPP) problem. The goal of the CPP problem is to find paths for each drone such that the entire area is covered. However, a major limitation in such deployments is drone flight time. To most efficiently use a swarm, we propose to minimize the maximum energy consumption among all drones' flight paths. We perform measurements to understand energy consumption of a drone. Using these measurements, we formulate an Energy Efficient Coverage Path Planning (EECPP) problem. We solve this problem in two steps: a load-balanced allocation of the given area to individual drones, and a minimum energy path planning (MEPP) problem for each drone. We conjecture that MEPP is NP-hard as it is similar to the Traveling Salesman Problem (TSP). We propose an adaptation of the well-known Lin-Kernighan heuristic for the TSP to efficiently solve the problem. We compare our solution to the recently proposed depth-limited search with back tracking algorithm, the optimal solution, and rastering as a baseline. Results show that our algorithm is more computationally efficient and provides more energy-efficient solutions compared to the other heuristics.

Millimeter-wave (mmWave) communications is one of the most promising candidate technologies for next generation cellular networks due to the global bandwidth shortage for mobile broadband access. The susceptibility of millimeter waveform propagation to blockages, however, may largely restrict the coverage of mmWave signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we develop a coverage probability model for the downlink of a relay- assisted mmWave cellular network using dominant interferer analysis, which accounts for both beamforming gains and blockages. Theoretical analysis and simulation results show that the downlink coverage of a mmWave cellular network can be improved by using two-hop D2D relay transmissions.

Supporting massive numbers of machine-type communication (MTC) devices poses several challenges for future 5G networks, including network control, scheduling, and powering these devices. A potential solution is to offload MTC traffic onto device-to-device (D2D) communication links to better manage radio resources and reduce MTC devices' energy consumption. However, this approach requires D2D users to use their own limited energy to relay MTC traffic, which may be undesirable. This motivates us to exploit recent advancements in RF energy harvesting for powering D2D relay transmissions. In this paper, we consider a D2D communication as an underlay to the cellular network, where D2D users access a fraction of the spectrum occupied by cellular users. This underlay model presents a fundamental trade-off: to protect cellular users, the spectrum available to D2D users needs to be reduced, which limits the number of D2D transmissions, but increases the amount of time that D2D users can spend harvesting energy to support MTC traffic. We study this trade-off by characterizing the spectral efficiency of MTC, D2D, and cellular users using stochastic geometry. The optimal spectrum partition factor is characterized to achieve fairness and balance in the network, while increasing the average MTC spectral efficiency.

In a massive machine type communication (massive MTC) network, wireless connections between machine type devices (MTDs) and eNodeBs are unreliable due to the interference caused by uncoordinated access of other MTDs. Furthermore, it is with a high probability that the retransmission from the outage source MTD will fail again due to the spatiotemporally correlated interference. In this paper, we design and analyze a location-based cooperative strategy to improve the performance of massive MTC networks. In the cooperative strategy, an inactive MTD is selected as a relay if it has successfully decoded the packet and if it is located within a circular area around the eNodeB. Considering the spatial and temporal correlation of interference, the outage probability of the designed cooperative strategy is derived using stochastic geometry. Both the simulation and numerical results demonstrate that spatiotemporal correlation of interference significantly affects the performance analysis of cooperative massive MTC networks and our designed cooperative strategy can significantly reduce the outage probability compared to conventional retransmission.

We study learning-based energy-efficient multi- user scheduling of delay-sensitive data over fading channels. To tradeoff energy and delay, we combine adaptive rate transmission at the physical layer with a rate-adaptive medium access control (MAC) protocol based on carrier sense multiple access with collision avoidance (CSMA/CA). We formulate the multi-user scheduling problem as a constrained Markov decision process (CMDP). We show that the multi-user problem is intractable and propose to decompose it into multiple (coupled) single-user problems. We design a reinforcement learning algorithm to solve the single-user problems online so that users can achieve energy-efficient operation while meeting their delay constraints, even though the channel, traffic, and multi-user dynamics are unknown a priori. Our proposed MAC protocol enables users to meet significantly tighter delay constraints while also consuming less energy than under the 802.11 Distributed Coordination Function (DCF). Moreover, the proposed learning algorithm converges significantly faster than a state-of-the-art solution.

In this paper, we consider an energy challenged remote sensor transmitting latency-sensitive imagery data over a time-varying channel. The sensor harvests energy from the environment and hence efficient energy consumption is of great importance. In this paper, we aim to find the optimal transmission scheduling and power management policies that maximize the available energy for future transmissions while meeting a queuing delay constraint. We formulate this problem as a Markov Decision Process (MDP) and propose a reinforcement learning (RL) algorithm to solve it online. Our experiments show that the proposed algorithm achieves comparable performance to a state-of-the-art RL algorithm, but at much lower complexity.

We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to “buy” and “sell” downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policyonline, which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20 percent throughput gains on average compared to B2D communications alone, all while selfishly maximizing their own utilities.

We formulate the problem of point-to-point scheduling at a congested network node as a Markov decision process (MDP) that considers the deadlines and priorities of each packet as well as the dynamic packet arrivals and channel conditions. Within this framework, we formulate the problem with the objective of maximizing the node's long-run priority-weighted throughput subject to instantaneous transmission rate constraints. We then analyze the structural properties of the optimal scheduling policy with respect to the deadlines and priorities of the backlogged packets. Additionally, we compare our approach to existing heuristics such as Priority Queueing (PQ), Earliest Deadline First (EDF), and Weighted Fair Queueing (WFQ). Our MDP-based approach outperforms all three heuristics not only because it takes into account the packets' priorities and deadlines, but also because it takes into account the future channel and packet arrival dynamics. Lastly, we experimentally show that the optimal scheduling policy has a switch-over type structure in several key parameters including the relative priorities of different traffic classes, the discount factor, and the traffic load intensity.

Numerous Directed-Acyclic Graph (DAG) schedulers have been developed to improve the energy efficiency of various multi-core systems. However, the DAG monitoring modules proposed by these schedulers make a priori assumptions about the workload and relationship between the task dependencies. Thus, schedulers are limited to work on a limited subset of DAG models. To address this problem, we propose a unified online DAG monitoring solution independent from the connected scheduler and able to handle all possible DAG models. Our novel low-complexity solution processes online the DAG of the application and provides relevant information about each task that can be used by any scheduler connected to it. Using H.264/AVC video decoding as an illustrative application and multiple configurations of complex synthetic DAGs, we demonstrate that our solution connected to an external simple energy-efficient scheduler is able to achieve significant improvements in energy-efficiency and deadline miss rates compared to existing approaches.

Numerous directed acyclic graph (DAG) schedulers have been developed to improve the energy efficiency of various multicore platforms. However, these schedulers make a priori assumptions about the relationship between the task dependencies, and they are unable to adapt online to the characteristics of each application without offline profiling data. Therefore, we propose a novel energy-efficient online scheduling solution for the general DAG model to address the two aforementioned problems. Our proposed scheduler is able to adapt at run-time to the characteristics of each application by making smart foresighted decisions, which take into account the impact of current scheduling decisions on the present and future deadline miss rates and energy efficiency. Moreover, our scheduler is able to efficiently handle execution with very limited resources by avoiding scheduling tasks that are expected to miss their deadlines and do not have an impact on future deadlines. We validate our approach against state-of-the-art solutions. In our first set of experiments, our results with the H.264 video decoder demonstrate that the proposed low-complexity solution for the general DAG model reduces the energy consumption by up to 15% compared to an existing sophisticated and complex scheduler that was specifically built for the H.264 video decoder application. In our second set of experiments, our results with different configurations of synthetic DAGs demonstrate that our proposed solution is able to reduce the energy consumption by up to 55% and the deadline miss rates by up to 99% compared to a second existing scheduling solution. Finally, we show that our DAG flow manager and scheduler have low complexities on a real mobile platform and we show that our solution is resilient to workload prediction errors by using different estimator accuracies.

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.

We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g., multimedia data) over a fading channel. Existing research on this topic utilizes either physical-layer centric solutions, namely power-control and adaptive modulation and coding (AMC), or system-level solutions based on dynamic power management (DPM); however, there is currently no rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to achieve the minimum possible energy consumption, under delay constraints, in the presence of stochastic and a priori unknown traffic and channel conditions. In this paper, we propose such a framework. We formulate the stochastic optimization problem as a Markov decision process (MDP) and solve it online using reinforcement learning (RL). The advantages of the proposed online method are that 1) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; 2) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and 3) it obviates the need for action exploration, which severely limits the adaptation speed and runtime performance of conventional reinforcement learning algorithms. Our results show that the proposed learning algorithms can converge up to two orders of magnitude faster than a state-of-the-art learning algorithm for physical layer power-control and up to three orders of magnitude faster than conventional reinforcement learning algorithms.

We consider the problem of energy-efficient on-line scheduling for slice-parallel video decoders on multicore systems with Dynamic Voltage Frequency Scaling (DVFS) enabled processors. In the past, scheduling and DVFS policies in multi-core systems have been formulated heuristically due to the inherent complexity of the on-line multicore scheduling problem. The key contribution of this paper is that we rigorously formulate the problem as a Markov decision process (MDP), which simultaneously takes into account the on-line scheduling and per-core DVFS capabilities; the power consumption of the processor cores and caches; and the loss tolerant and dynamic nature of the video decoder. The objective of the MDP is to minimize long-term power consumption subject to a minimum Quality of Service (QoS) constraint related to the decoder's throughput. We evaluate the proposed on-line scheduling algorithm in Matlab using realistic video decoding traces generated from a cycle-accurate multiprocessor ARM simulator.

On-demand video services such as Youtube and Hulu are expected to comprise a large percentage of the increasing data loads in mobile networks. On-demand video is distinctive because it is pre-recorded and therefore can be considered elastic traffic because the video frame buffer can be downloaded well past the current point of playback. Based on this observation, we propose Video Rest-and-Download (VR&D) as a video download application framework that aims to reduce network congestion while maintaining playback quality. The intuition for VR&D is that, in a scenario where radio resources are shared by multiple data users, the video user can “rest” for some amount of time until fewer users are in the network, thereby allowing other data users to complete their downloads faster, without affecting playback quality. We present an algorithmic framework for VR&D based on the Markov Decision Process that uses the history and current state of network activity to determine how aggressive the user should be in downloading video frames. We evaluate its performance using a simulated UMTS network with HSDPA data service based on real network traces from a major U.S. carrier. Our results show that, compared to the existing solution, during the time of video playback this application can reduce download time by as high as 50%, and alleviate network congestion by up to 30% with minimal effect on playback quality.

We investigate the impact of cooperative relaying on uplink multi-user (MU) wireless video transmissions. We analyze and simplify a MU Markov decision process (MDP), whose objective is to maximize the long-term sum of utilities across the video terminals in a decentralized fashion, by jointly optimizing the packet scheduling and physical layer, under the assumption that some nodes are willing to act as cooperative relays. The resulting MU-MDP is a pricing-based distributed resource allocation algorithm, where the price reflects the expected future congestion in the network. Compared to a non-cooperative setting, we observe that the resource price increases in networks supporting low transmission rates and decreases for high transmission rates. Additionally, cooperation allows users with feeble direct signals to significantly improve their video quality, with a moderate increase in total network energy consumption that is far less than the energy these nodes would require to achieve the same video quality without cooperation.

We investigate the impact of cooperative relaying on uplink multi-user (MU) wireless video transmission. We formulate the problem as an MU Markov decision process (MDP) that explicitly considers the cooperation at the physical layer and the medium access control sublayer, the video users' heterogeneous traffic characteristics, and the dynamically varying network conditions. Although MDPs notoriously suffer from the curse of dimensionality, our study shows that the complexity of the MU-MDP can be mitigated. Our simulation results show that cooperation allows users with feeble direct signals to achieve improvements in video quality on the order of 5-10 dB peak signal-to-noise ratio, with less than 0.8 dB quality loss by users with strong direct signals.

In this paper, the problem of scalable video delivery over a time-varying wireless channel is considered. Packet scheduling and buffer management in both Application and Medium Access Control (MAC) layers are jointly considered. Various levels of knowledge of the state of the channel are considered. The control is performed via scalable layer filtering (some scalability layers may be dropped). In all cases, the problem is cast in the context of Markov Decision Processes which allows the design of foresighted policies maximizing some long-term reward. Without channel state observation, the control has to rely on the observation of the level of the MAC buffer only. Experimental results show that even with a lack of knowledge of the channel state, the foresighted control policy provides only a moderate loss in received video quality.

We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. We propose a rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to minimize energy consumption, under delay constraints, in the presence of stochastic and unknown traffic and channel conditions. We formulate the problem as a Markov decision process and solve it online using reinforcement learning. The advantages of the proposed online method are that it exploits partial information about the system and it obviates the need for action exploration. Consequently, it significantly outperforms existing reinforcement learning solutions.

We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. We propose a rigorous and unified framework for simultaneously utilizing both physical-layer and system-level techniques to minimize energy consumption, under delay constraints, in the presence of stochastic and unknown traffic and channel conditions. We formulate the problem as a Markov decision process and solve it online using reinforcement learning. The advantages of the proposed online method are that (i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal physical-layer and system-level power management strategies; (ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and (iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms.

We formulate the multimedia buffer control problem as a Markov decision process. Because the application's rate-distortion-complexity behavior is unknown a priori, the optimal buffer control policy must be learned online. To this end, we adopt a low complexity reinforcement learning algorithm called Q-learning to learn the optimal control policy at run-time. We propose an accelerated Q-learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the performance. In our experiments, we show that the proposed application-aware reinforcement learning algorithm performs significantly better than existing application-independent reinforcement learning algorithms.

In recent years, cross-layer multimedia system design and optimization has garnered significant attention; however, there is no existing rigorous methodology for optimizing two or more system layers (e.g. the application, operating system, and hardware layers) jointly while maintaining a separation among the decision processes of each layer. Moreover, existing work often relies on myopic optimizations, which ignore the impact of decisions made at the current time on the system's future performance. In this paper, we propose a novel systematic framework for jointly optimizing the different system layers to improve the performance of one multimedia application. In particular, we model the system as a layered Markov Decision Process (MDP), which enables each layer to make autonomous and foresighted decisions that optimize the system's long-term performance.

Recently, much progress has been made on the cross-layer optimization of video streams in multiple access networks. The key idea is to use the granular data structure of compressed video to trade quality with bits, and optimally prioritize transmissions given the available bandwidth, in order to obtain proportionally optimal video quality across video streams. Herein, we discuss the effect and potential benefit of using a cooperative-relay strategy in a wireless network, where proportionally optimal video schedules are computed via a multi-user Markov decision process. The idea is that feeble signals of nodes that are located far away from the destination can be enhanced via the cooperation of intermediate nodes, acting as cooperative relays. Our contribution is to indicate a possible solution that would require relatively modest changes to the multi-user optimization framework, while warranting a uniformly better experience to the video users thanks to cooperative coding.

This paper addresses the problem of video on demand delivery over a time-varying wireless channel. Packet scheduling and buffer management are jointly considered for scalable video transmission to adapt to the changing channel conditions. A proxy-based filtering algorithm among scalable layers is considered to maximize the decoded video quality at the receiver side while keeping a minimum playback margin. This problem is cast in the context of Markov Decision Processes which allows the design of foresighted policies maximizing some long-term reward. Experimental results illustrate the benefit of this approach compared to a shortterm policy in term of average PSNR improvement.

In our recent 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 (e.g. the application's rate-distortion-complexity behavior) were known a priori, by modeling the system as a layered Markov decision process. 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. In our experiments, we demonstrate that decentralized learning can perform equally as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing myopic learning algorithms deployed in multimedia systems perform significantly worse than our proposed foresighted learning methods.

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, by modeling the system as a layered Markov decision process. 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. The two key challenges in this layered learning setting are: (i) each layer's learning performance is directly impacted by not only its own dynamics, but also by the learning processes of the other layers with which it interacts; and (ii) selecting a learning model that appropriately balances time-complexity (i.e., learning speed) with the multimedia system's limited memory and the multimedia application's real-time delay constraints. 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 interlayer 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 equally 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.

In recent years, cross-layer multimedia system design and optimization has garnered significant attention; however, there exists no rigorous methodology for optimizing two or more system layers (e.g., the application, operating system, and hardware layers) jointly while maintaining a separation among the decision processes, designs, and implementations of each layer. Moreover, existing work often relies on myopic optimizations, which ignore the impact of decisions made at the current time on the system's future performance. In this paper, we propose a novel systematic framework for jointly optimizing the different system layers to improve the performance of one multimedia application. In particular, we model the system as a layered Markov decision process (MDP). The proposed layered MDP framework enables each layer to make autonomous and foresighted decisions, which optimize the system's long-term performance.

We investigate the problem of providing media services to multiple autonomous wireless users at the edge of a content delivery network (CDN) in a setting where wireless resources are priced based on real-time market demands. Our focus is on the multimedia service resource negotiation process, which is performed prior to the actual media transmission. We adopt the progressive second price (PSP) auction mechanism, which is used to determine the network resource allocation to the users and a corresponding tax for the consumed resources. Our interest in this negotiation mechanism lies in understanding a single user's (oragent's) ability to learn to improve its bids over time in order to increase its own utility in the face of time-varying resource valuations and contention for resources with other users. We pay particular attention to the implementation complexity and the information requirements of the agent's deployed learning rule, and we quantify the impact of these factors on the rule's ultimate performance (i.e., the cumulative utility achieved over time) and efficiency (i.e., the utility gained per unit of computation). These factors are especially important in the mobile video streaming context, where limited resources must be efficiently utilized, and where communication and computation overheads can significantly impact the quality of service experienced by the user.

We propose a new complexity modeling framework for multimedia tasks. We characterize the traffic with five parameters that together we designate as a task's complexity specification (CSPEC). We extend this model to a scalable CSPEC, which can be used to characterize the many complexity- and quality-scalable operating points available to multimedia tasks. The proposed scalable CSPEC can be used by multimedia applications to match their resource requirements to available system resources.

In this paper, we propose a new resource allocation framework for multimedia systems that perform multiple simultaneous video decoding tasks. We jointly consider the available system resources (e.g., processor cycles) and the video decoding task's characteristics such as the sequence's content, the bit-rate, and the group of pictures (GOP) structure, in order to determine a fair and optimal resource allocation. To this end, we derive a quality-complexity model that determines the quality [in terms of peak signal-to-noise ratio (PSNR)] that a task can achieve given a certain system resource allocation. We use these quality-complexity models to determine a quality-fair and Pareto-optimal resource allocation using the Kalai–Smorodinski Bargaining Solution (KSBS) from axiomatic bargaining theory. The KSBS explicitly considers the resulting multimedia quality when performing a resource allocation and distributes quality-domain penalties proportional to the difference between each video decoding task's maximum and minimum quality requirements. We compare the KSBS with other fairness policies in the literature and find that, because it explicitly considers multimedia quality, it provides significantly fairer resource allocations in terms of the resulting PSNR compared with policies that operate solely in the resource domain. To weight the quality impact of the resource allocations to the different decoding tasks depending on application-specific requirements or user preferences, we generalize the existing KSBS solution by introducing bargaining powers based on each video sequence's motion and texture characteristics.

Peer-to-peer collaboration paradigms fundamentally change the passive way wireless stations currently adapt their transmission strategies to match available resources, by enabling them to proactively influence system dynamics through exchange of information and resources. In this paper, we focus on delay-sensitive multimedia transmission among multiple peers over wireless multi-hop enterprise mesh networks. We propose a distributed and efficient framework for resource exchanges that enables peers to collaboratively distribute available wireless resources among themselves based on their quality of service requirements, the underlying channel conditions, and network topology. The resource exchanges are enabled by the scalable coding of the video content and the design of cross-layer optimization strategies, which allow efficient adaptation to varying channel conditions and available resources. We compare our designed low complexity distributed resource exchange algorithms against an optimal centralized resource management scheme and show how their performance varies with the level of collaboration among the peers. We measure system utility in terms of the multimedia quality and show that collaborative approaches achieve ~50% improvement over non-collaborative approaches. Additionally, our distributed algorithms perform within 10% system utility of a centralized optimal resource management scheme. Finally, we observe 2-5 dB improvement in decoded PSNR for each peer due to the deployed cross-layer strategy

We propose a cross-layer design for resource-constrained systems that simultaneously decode multiple video streams on multiple parallel processors, cores, or processing elements. Our proposed design explicitly considers the coder specific application characteristics such as the decoding dependencies, decoding deadlines, and distortion impacts of different video packets (e.g., frames, slices, groups of slices etc.). The key to the cross-layer design is the resource management control plane (RMCP) that coordinates the scheduling and processor selection across the active applications. The RMCP deploys a priority-queuing model that can evaluate the system congestion and predict the total expected video quality for the set of active decoding tasks. Using this model, we develop a robust distortion- and delay-aware scheduling algorithm for video packets. This algorithm aims to maximize the sum of achieved video qualities over all of the decoded video sequences. Additionally, we propose a processor selection scheme intended to minimize the delays experienced by the queued video packets. In this way, the number of missed decoding deadlines is reduced and the overall decoded video quality is increased. We compare queuing-theoretic based scheduling strategies to media agnostic scheduling strategies (i.e., earliest-deadline-first scheduling) that do not jointly consider the decoding deadlines and distortion impacts. Our results illustrate that by directly considering the video application's properties in the design of a video decoding system, significant system performance gains on the order of 4 dB peak-signal-to-noise ratio can be achieved.

We propose an integrated cross-layer optimization algorithm for maximizing the decoded video quality of delay-constrained streaming in a quality-of-service (QoS) enabled multi-hop wireless mesh network. The key to our algorithm is the synergistic optimization of control parameters at each node of the multi-hop network, across the protocol layers - application, network, medium access control (MAC) and physical (PHY) layers, as well as end-to-end, i.e. across the various network nodes. To drive this optimization, we assume an overlay network infrastructure, which conveys information on the conditions of each link. Quantitative results are presented that demonstrate the merits and the need for cross-layer optimization in an efficient solution for real-time video transmission using existing protocols and infrastructures

In this paper, we consider the problem of real-time multimedia transmission among several peers (users). The peers use a heterogeneous wireless multi-hop mesh network for the delivery of these high-bandwidth streams. One of the main challenges of the considered problem is the division of the scarce wireless resources among the various peers. To address this problem, we propose an efficient, distributed and collaborative framework for wireless resource exchanges that enables peers to divide available wireless resources among themselves based on their quality of service (QoS) requirements, the underlying channel conditions and network topology. The scalable coding of the video content and decomposition of video flows into various sub-flows (priorities) allow peers to transfer the video at different quality levels, depending on the network load. Users collaboratively decide which of their sub-flows to admit, and which paths these sub-flows should be transmitted on in order to maximize a system defined utility. Our results show that with user collaboration, these distributed algorithms provide system and user performance comparable to a centralized exhaustive implementation.

The proliferation of wireless multihop communication infrastructures in office or residential environments depends on their ability to support a variety of emerging applications requiring real-time video transmission between stations located across the network. We propose an integrated cross-layer optimization algorithm aimed at maximizing the decoded video quality of delay-constrained streaming in a multihop wireless mesh network that supports quality-of-service. The key principle of our algorithm lays in the synergistic optimization of different control parameters at each node of the multihop network, across the protocol layers-application, network, medium access control, and physical layers, as well as end-to-end, across the various nodes. To drive this optimization, we assume an overlay network infrastructure, which is able to convey information on the conditions of each link. Various scenarios that perform the integrated optimization using different levels ("horizons") of information about the network status are examined. The differences between several optimization scenarios in terms of decoded video quality and required streaming complexity are quantified. Our results demonstrate the merits and the need for cross-layer optimization in order to provide an efficient solution for real-time video transmission using existing protocols and infrastructures. In addition, they provide important insights for future protocol and system design targeted at enhanced video streaming support across wireless mesh networks