Video streaming applications have become immensely popular, leading to increasing user expectations for high-quality services. Extensive work has been conducted in the areas of Quality of Experience (QoE) modeling and Adaptive Bitrate (ABR) algorithms to meet this demand. While learningbased approaches have demonstrated substantial progress using large-scale datasets, existing QoE models often focus on systemlevel metrics such as bitrate and resolution within the playback buffer, neglecting the quality as perceived by the human eye. Simultaneously, many learning-based ABR algorithms exhibit limited robustness in dynamic environments due to their reliance on a one-size-fits-all strategy, which fails to adapt effectively to complex, real-world conditions. In this paper, we propose an integrated system that addresses these limitations by combining an accurate QoE model with an environment-robust adaptation algorithm to enhance user satisfaction in diverse and dynamic environments. First, we introduce RetQoE, a novel approach that accurately estimates the user’s actual QoE by focusing on the quality of video content as perceived by the viewer, rather than on conventional system metrics. Then, we design PVA, a meta-reinforcement learning-based adaptation that rapidly adjusts its policy to varying environments. We systematically integrate RetQoE and PVA, enabling PVA to update its policy with feedback from RetQoE in just a few steps online. We demonstrate the effectiveness of RetQoE+PVA through extensive evaluations in diverse environments, outperforming conventional learning-based algorithms across various metrics.

Deploying high-spec cameras in video systems often falls short of user expectations. Leveraging advancements in deep learning, we propose a mobile, lightweight, real-time video enhancement system. Our approach adopts cutting-edge models and introduces novel optimization techniques for real-time streaming, improving low-resolution, grayscale, and low frame-rate videos. Preliminary evaluations show significant improvements in PSNR and SSIM, while visual assessments confirm substantial quality enhancements while maintaining real-time processing requirements.

The shift from traditional PCs and TVs to mobile devices such as smartphones and tablets has significantly transformed the video streaming landscape. Traditional Quality of Experience (QoE) models, predominantly designed for larger screens, fall short in addressing the nuances of mobile consumption, often misguiding bandwidth usage and quality delivery. This paper introduces a novel user-oriented QoE model tailored for the mobile environment, which accounts for heterogeneous viewing environments. Unlike conventional models that estimate QoE based solely on bitrate and resolution, our approach encompasses the entire video streaming pipeline, from server transmission to the user's perception. In addition, we design lightweight but effective QoE models for mobile devices. This work bridges the gap between user experience and QoE modeling, offering a path toward more adaptive and efficient video streaming services for the increasingly mobile-centric world.

The prevalence of video streaming applications has led to an escalation in users' demands for high-quality services. Numerous endeavors have been undertaken in the realm of quality-of-experience (QoE) models and adaptive bitrate (ABR) algorithms to fulfill this demand. Nevertheless, the existing QoE models exhibit a significant gap with users' actual experience. ABR algorithms are vulnerable in dynamic network environments. We present an integrated system with an accurate QoE model and an environment-robust adaptation algorithm to ensure high user satisfaction in dynamic network conditions. We define a QoE model that accurately estimates the user's QoE by considering the viewing environment and video content. We then design a meta-reinforcement learning-based adaptation algorithm that adapts to dynamic network conditions. We systematically integrate them, allowing it to update its policy with QoE feedback within a few shots.

The adaptation of deep convolutional neural network has made revolutionary advances in human body posture estimation. Various applications utilizing deep neural network for pose estimation have drawn considerable attention in recent years. However, prediction and localization of keypoints in single-person and multi-person images is still a challenging problem. Towards this, we propose a bottom-up approach to pose estimation and motion recognition. We present StrongPose system that deals with object-part associations using part-based modeling. The convolution network in our model detects strong keypoint heat maps and predicts their comparative displacements, allowing keypoints to be grouped into human instances. Further, it utilizes the keypoints to generate body heat maps that can determine the position of the human body in the image. The StrongPose system is based on fully convolutional engineering and makes proficient inferences while maintaining runtime regardless of the number of individuals in the image. We train and test the StrongPose on the COCO dataset. Evaluation results show that our framework achieves average precision of 0.708 using ResNet-101 and 0.725 using ResNet-152. Our results considerably outperform prior bottom-up frameworks.

Wireless video streaming has become an extremely popular application in recent years. Internet video streaming to mobile devices, however, faces several challenges, e.g., unstable wireless connections, long latency, high jitter and etc. Bitrate adaptive streaming and video transcoding solutions are widely used to address the above-mentioned issues, however, there are still several shortcomings of these approaches. Such challenges hinder providing satisfactory quality of video streaming service to the mobile users. We propose a hardware-assisted, real-time video transcoding solution implemented on a commercial off-the-shelf device, Raspberry Pi. We employ the software and hardware coupled architecture in order to improve the performance/quality of video streaming and enhance the user satisfactions in wireless network. Our video transcoding solution can be applied to both the downlink and uplink streaming: for downlink stream, it can provide agile bitrate adaptation to sudden network dynamics and enhance video quality by running our transcoding solution at the wireless edge. It can be used to uplink stream for broadcasting live streams in real-time. We present the design and implementation of our video transcoding system in both cases with practical scenarios. The evaluation results reveal that our transcoding solution enhances the performance of video streaming compared with other adaptive bitrate streamings and it provides higher video quality without causing rebuffering or video stall. We bridge the gap between the wireless channel capacity and the video quality while providing a better streaming experience to end user.

In recent years, device-free tracking has been studied for convenience and needs in a variety of areas including the AR, VR, and entertainment industries. Motion tracking with the camera provides good performance, however there are some restrictions caused by lighting. In addition, there is a drawback that the amount of calculation is also high as much data is input. Many approaches have been proposed to overcome these shortcomings. Acoustic signal-based tracking is a good alternative because it consumes relatively low energy and does not require additional equipment. Signal-based tracking measures the relative distance of an object using the phase difference of the received signals. There are still several challenges when estimating the absolute distance to an object, and hence the accuracy is not guaranteed. We propose a peak signal-based estimation method that is very suitable for mobile devices with very accurate and low computational complexity. Experiment results show high accuracy and feasibility.

Video streaming is one of the killer applications in recent years. Video transcoding plays an important role in the video streaming service to cope with the various purposes. Specifically, content owners and publishers heavily utilize video transcoders to reconfigure source video in a variety of formats, video qualities, and bitrate to provide end users with the best possible quality of service. In this paper, we present VideoCoreCluster, a low‐cost and energy‐efficient transcoder cluster that is suitable for live streaming services. We designed and implemented real‐time video transcoder cluster using cheap ($35), powerful, and energy‐efficient Raspberry Pi. The quality of transcoded video provided by VideoCoreCluster is similar to the best software‐based video transcoder while consuming significantly less energy (<3 W). We have proposed a scheduling algorithm based on priority of video stream and transcoding capacity. Our cluster manager provides reliable and scalable streaming services, because it uses the characteristics of adaptive bitrate scheme. We have deployed our transcoding cluster to provide IP‐based TV streaming services on our university campus.

Small cells form a critical component of next generation cellular networks, where spatial reuse is the key to higher spectral efficiencies. Interference management in the spatial domain through beamforming allows for increased reuse without having to sacrifice resources in the time or frequency domain. Existing beamforming techniques for spatial reuse, being coupled with client scheduling, face a key limitation in practical realization, especially with OFDMA small cells. In this context, we argue that for a practical spatial reuse system with beamforming, it is important to decouple beamforming from client scheduling. Further, we show that jointly addressing client association with beamforming is critical to maximizing the reuse potential of beamforming. Towards our goal, we propose ProBeam - a system for multi-cell beamforming and client association in OFDMA small cell networks. ProBeam incorporates two key components - a low complexity, highly accurate SINR estimation module that helps determine interference dependencies for beamforming between small cells; and an efficient, low complexity joint client association and beam selection algorithm for the small cells that accounts for scheduling at the small cells without being coupled with it. We have prototyped ProBeam on a WiMAX-based network of four small cells. Our evaluations reveal the accuracy of our SINR estimation module to be within 1 dB, and the reuse gains from joint client association and beamforming to be as high as 115 percent over baseline approaches.

Next generation wireless networks (i.e., WiMAX, LTE) provide higher bandwidth and spectrum efficiency leveraging smaller (femto) cells with orthogonal frequency division multiple access (OFDMA). The uncoordinated, dense deployments of femtocells however, pose several unique challenges relating to interference and resource management in OFDMA femtocell networks. Towards addressing these challenges, we propose RADION, a distributed resource management framework that effectively manages interference across femtocells. RADION's core building blocks enable femtocells to opportunistically determine the available resources in a completely distributed and efficient manner. Further, RADION's modular nature paves the way for different resource management solutions to be incorporated in the framework. We implement RADION on a real WiMAX femtocell testbed deployed in a typical indoor setting. Two distributed solutions are enabled through RADION and their performance is studied to highlight their quick self-organization into efficient resource allocations.

We present WiScape, a framework for benchmarking and understanding the behavior of wide-area wireless networks, e.g., city-wide or nation-wide cellular data networks using active participation from clients. The goal of WiScape is to provide a coarse-grained view of a wide-area wireless landscape that allows operators and users to understand broad performance characteristics of the network. In WiScape, a centralized controller instructs clients to collect minimal measurement samples over time and space in an opportunistic manner. To limit the overheads of this measurement framework, WiScape partitions the world into zones, contiguous areas with relatively similar user experiences, and partitions time into zone-specific epochs over which network statistics are relatively stable. For each epoch in each zone, WiScape takes a minimalistic view-it attempts to collect a small number of measurement samples to characterize the client experience in a zone at a specific epoch, thereby limiting the bandwidth and energy overheads for collecting measurements at client devices. For this effort, we have collected ground truth measurements for three commercial cellular networks across a nation-wide area in USA for a period of more than one year. We justify our design choices of WiScape through collected data, demonstrate that WiScape can provide an accurate performance characterization of the networks over a wide-area (within 4 percent error for more than 70 percent of instances) with a low overhead on the clients, and illustrate multiple applications of this framework through a sustained and ongoing measurement study.

Static analysis tools are useful to find potential bugs and security vulnerabilities in a source code, however, false alarms from such tools lower their usability. In order to reduce various kinds of false alarms and enhance the performance of the tools, we propose a machine learning based false alarm reduction method. Abstract syntax trees (AST) are used to represent structural characteristics and support vector machine (SVM) is used to learn models and classify new alarms using probability. This probability is used to remove false alarms. To evaluate the proposed method, we performed experiments using a static analysis tool, SPARROW, and Java open source projects. As a result, 37.33% of false alarms were reduced, with only removing 3.16% of true alarms.

The work in this paper is a systematic research and engineering effort in exploring the design space of multi-interface wireless repeater systems. We present the design, implementation and evaluation of a wireless repeater system, Multifacet, which opportunistically utilizes multiple interfaces to enhance capacity. The system is designed to be transparent to application endpoints and does not need any end application modifications for adoption. Multifacet incorporates several techniques to achieve efficient bandwidth utilization across multiple interfaces -- (a) coordinated channel sharing, (b) a simplified backpressure based striping technique, (c) a single link abstraction, and (d) the ability to seamlessly migrate a client to the optimal channel. Multifacet is implemented on off-the-shelf dual band wireless repeater and demonstrates high speeds (337 Mbps) operation. On average, Multifacet performs 50% better than traditional AP/repeater setups, and in the best case, more than 2x better.

Although wireless broadband technologies have evolved significantly over the past decade, they are still insufficient to support the fast-growing mobile traffic, especially due to the increasing popularity of mobile video applications. Wireless multicast, aiming to exploit the wireless broadcast advantage, is a viable approach to bridge the gap between the limited wireless capacity and the ever-increasing mobile video traffic demand. In this paper, we propose MuVi, a Multicast Video delivery scheme through joint optimal resource allocation and adaptive modulation and coding scheme in OFDMA-based 4G cellular networks. MuVi differentiates video frames based on their importance in reconstructing the video and incorporates an efficient radio resource allocation algorithm to optimize the overall video quality across all users in the multicast group. MuVi is a lightweight solution with most of the implementation in the gateway, slight modification in the base station, and no modification at the clients. We implement MuVi on a WiMAX testbed and compare its performance to a Naive wireless multicast scheme that employs the most robust Modulation and Coding Scheme (MCS), and an Adaptive scheme that employs the highest MCS supportable by all clients. Experimental results show that MuVi improves the average video peak signal-to-noise ratio (PSNR) by up to 13 and 7 dB compared to the Naive and the Adaptive schemes, respectively. MuVi does not require modification to the video encoding scheme or the air interface. Thus, it allows speedy deployment in existing systems.

Small cells form a critical component of next generation cellular networks, where spatial reuse is the key to higher spectral efficiencies. Interference management in the spatial domain through beamforming allows for increased reuse without having to sacrifice resources in the time or frequency domain. Existing beamforming techniques for spatial reuse, being coupled with client scheduling, face a key limitation in practical realization, especially with OFDMA small cells. In this context, we argue that for a practical spatial reuse system with beamforming, it is important to decouple beamforming from client scheduling. Further, we show that jointly addressing client association with beamforming is critical to maximizing the reuse potential of beamforming.

To meet the capacity demands from ever-increasing mobile data usage, mobile network operators are moving toward smaller cell structures. These small cells, called femtocells, use sophisticated air interface technologies such as orthogonal frequency division multiple access (OFDMA). While femtocells are expected to provide numerous benefits such as energy efficiency and better throughput, the interference resulting from their dense deployments prevents such benefits from being harnessed in practice. Thus, there is an evident need for a resource management solution to mitigate the interference that occurs between collocated femtocells. In this paper, we design and implement one of the first resource management systems, FERMI, for OFDMA-based femtocell networks. As part of its design, FERMI: 1) provides resource isolation in the frequency domain (as opposed to time) to leverage power pooling across cells to improve capacity; 2) uses measurement-driven triggers to intelligently distinguish clients that require just link adaptation from those that require resource isolation; 3) incorporates mechanisms that enable the joint scheduling of both types of clients in the same frame; and 4) employs efficient, scalable algorithms to determine a fair resource allocation across the entire network with high utilization and low overhead. We implement FERMI on a prototype four-cell WiMAX femtocell testbed and show that it yields significant gains over conventional approaches.

The increase in mobile data usage is pushing broadband operators towards deploying smaller cells (femtocells) and sophisticated access technologies such as OFDMA. The expected high density of deployment and uncoordinated operations of femtocells however, make interference management both critical and extremely challenging. Femtocells have to use the same access technology as traditional macrocells. Given this, understanding the impact of the system design choices (originally tailored to well-planned macrocells) on interference management, forms an essential first step towards designing efficient solutions for next-generation femtocells. This in turn is the focus of our work. With extensive measurements from our WiMAX OFDMA femtocell testbed, we characterize the impact of various system design choices on interference. Based on the insights from our measurements, we discuss several implications on how to efficiently operate a femtocell network.

Although wireless broadband technologies have evolved significantly over the past decade, they are still insufficient to support the fast-growing mobile traffic, especially due to the increasing popularity of mobile video applications. Wireless multicast, aiming to exploit the wireless broadcast advantage, is a viable approach to bridge the gap between the limited wireless networking capacity and the ever-increasing mobile video traffic demand. In this work, we propose MuVi, a Multicast Video delivery scheme in OFDMA-based 4G wireless networks, to optimize multicast video traffic. MuVi differentiates video frames based on their importance in reconstructing the video and incorporates an efficient radio resource allocation algorithm to optimize the overall video quality across all users in the multicast group. MuVi is a lightweight solution with most of the implementation in the gateway, slight modification in the base-station, and no modification at the clients. We implement MuVi on a WiMAX testbed and compare its performance to a Naive wireless multicast scheme that employs the most robust MCS (Modulation and Coding Scheme), and an Adaptive scheme that employs the highest MCS supportable by all clients. Experimental results show that MuVi improves the average video PSNR (Peak Signal-to-Noise Ratio) by up to 13 and 7 dB compared to the Naive and the Adaptive schemes, respectively. MuVi does not require modification to the video encoding scheme or the air interface. Thus it allows speedy deployment in existing systems.

Next generation wireless networks (i.e., WiMAX, LTE) provide higher bandwidth and spectrum efficiency leveraging smaller (femto) cells with orthogonal frequency division multiple access (OFDMA). The uncoordinated, dense deployments of femtocells however, pose several unique challenges relating to interference and resource management in these networks. Towards addressing these challenges, we propose RADION, a distributed resource management framework that effectively manages interference across femtocells. RADION's core building blocks enable femtocells to opportunistically find the available resources in a completely distributed and efficient manner. Further, RADION's modular nature paves the way for different resource management solutions to be incorporated in the framework. We implement RADION on a real WiMAX femtocell testbed deployed in a typical indoor setting. We extensively evaluate two solutions integrated with RADION, both via prototype implementation and simulations and quantify their performance in terms of quick and efficient self-organization.

Genetic algorithms (GA) are one of effective approaches to solve the traveling salesman problem (TSP). When applying GA to the TSP, it is necessary to use a large number of individuals in order to increase the chance of finding optimal solutions. However, this incurs high evaluation costs which make it difficult to obtain fitness values of all the individuals. To overcome this limitation we propose an efficient genetic algorithm based on fuzzy clustering which reduces evaluation costs with minimizing loss of performance. It works by evaluating only one representative individual for each cluster of a given population, and estimating the fitness values of the others from the representatives indirectly. A fuzzy c-means algorithm is used for grouping the individuals and the fitness of each individual is estimated according to membership values. The experiments were conducted with randomly generated cities, and the performance of the method was evaluated by comparing to other GAs. The results showed the usefulness of the proposed method on the TSP.

We present WiScape, a framework for measuring and understanding the behavior of wide-area wireless networks, e.g., city-wide or nation-wide cellular data networks using active participation from clients. The goal of WiScape is to provide a coarse-grained view of a wide-area wireless landscape that allows operators and users to understand broad performance characteristics of the network. In this approach a centralized controller instructs clients to collect measurement samples over time and space in an opportunistic manner. To limit the overheads of this measurement framework, WiScape partitions the world into zones, contiguous areas with relatively similar user experiences, and partitions time into zone-specific epochs over which network statistics are relatively stable. For each epoch in each zone, WiScape takes a minimalistic view --- it attempts to collect a small number of measurement samples to adequately characterize the client experience in that zone and epoch, thereby limiting the bandwidth and energy overheads at client devices. For this effort, we have collected ground truth measurements for up to three different commercial cellular wireless networks across (i) an area of more than 155 square kilometer in and around Madison, WI, in the USA, (ii) a road stretch of more than 240 kilometers between Madison and Chicago, and (iii) locations in New Brunswick and Princeton, New Jersey, USA, for a period of more than 1 year. We justify various design choices of WiScape through this data, demonstrate that WiScape can provide an accurate performance characterization of these networks over a wide area (within 4% error for more than 70% of instances) with a low overhead on the clients, and illustrate multiple applications of this framework through a sustained and ongoing measurement study.

The demand for increased spectral efficiencies is driving the next generation broadband access networks towards deploying smaller cells (femtocells) with sophisticated air interface technologies (Orthogonal Frequency Division Multiple Access or OFDMA). The projected dense deployment of femtocells however, makes interference and hence resource management both critical and extremely challenging. In this paper, we design and implement one of the first resource management systems, FERMI, for OFDMA-based femtocell networks. As part of its design, FERMI (i) provides resource isolation in the frequency domain (as opposed to time) to leverage power pooling across cells to improve capacity; (ii) uses measurement-driven triggers to intelligently distinguish clients that require just link adaptation from those that require resource isolation; (iii) incorporates mechanisms that enable the joint scheduling of both types of clients in the same frame; and (iv) employs efficient, scalable algorithms to determine a fair resource allocation across the entire network with high utilization and low overhead. We implement FERMI on a prototype four-cell WiMAX femtocell testbed and show that it yields significant gains over conventional approaches.

A genetic algorithm can be applied to various search or optimization problems. However, there exists a problem that it takes too much cost to evaluate a large number of individuals. To deal with the problem, the fitness approximation method which reduces the cost of the evaluation with the similar performance to the general GA is needed. We proposed the fitness approximation using a combination of the approximation model and the fuzzy clustering technique. There exist two advantages of the proposed method. First, it reduces the cost of the fitness evaluation. Second, it shows the similar performance to the general GA. To verify the performance of the method, we designed the experiments using several benchmark functions and compared other fitness approximation methods.

The IEEE 802.15.4 medium access control (MAC) protocol is designed for low data rate, short distance and low power communication applications such as wireless sensor networks (WSN). However, in the standard 802.15.4 MAC, if the remaining number of backoff periods in the current superframe are not enough to complete data transmission procedure, the sensor nodes hold the transmission until the next superframe. When two or more sensor nodes hold data transmission and restart the transmission procedure simultaneously in the next superframe, it causes a collision of data packets and waste of the channel utilization. Therefore, the MAC design is inadequate to deal with high contention environments such as densely deployed sensor networks. In this paper, we propose a data fragmentation scheme to increase channel utilization and avoid inevitable collision. Our proposed scheme outperforms the standard IEEE 802.15.4 MAC in terms of collision probability and aggregate throughput. The proposed scheme is easily adapted to the standard IEEE 802.15.4 MAC without any additional message types

The throughput performance of the distributed coordination function (DCF) of the IEEE 802.11 MAC protocol quickly degrades as the number of contending stations increases. To solve this problem, it has been shown recently that adaptive contention window modulation based on channel idle time tracking can be used, generating near optimal throughput. In this paper, we extend the approach for the IEEE 802.11e network, where different QoS classes are defined. We show how to find the class-specific optimal contention window sizes that yield the maximum aggregate throughput while maintaining the target throughput difference between classes