This paper considers standard T-interval dynamic networks, where the N nodes in the network proceed in lock-step rounds, and where the topology of the network can change arbitrarily from round to round, as determined by an adversary. The adversary promises that in every T consecutive rounds, the T (potentially different) topologies in those T rounds contain a common connected subgraph that spans all nodes. Within such a context, we propose novel algorithms for solving some fundamental distributed computing problems such as Count/Consensus/Max. Our algorithms are the first algorithms whose complexities do not contain an Ømega(N) term, under constant T values. Previous sublinear algorithms require significantly larger T values.

In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories. Previous HOI studies are mainly conducted in the regime of limit object categories (e.g., 80 categories). Their solutions may face new difficulties in both object detection and interaction classification due to the increasing diversity of objects (e.g., 1000 categories). Different from previous methods, we formulate the HOI detection as a query problem. We propose a unified model to jointly discover the target objects and predict the corresponding interactions based on the human queries, thereby eliminating the need of using generic object detectors, extra steps to associate human-object instances, and multi-stream interaction recognition. This is achieved by a repurposed Transformer unit and a novel cascade detection over multi-scale feature maps. We observe that such a highly-coupled solution brings benefits for both object detection and interaction classification in a large vocabulary setting. To study the new challenges of the large vocabulary HOI detection, we assemble two datasets from the publicly available SWiG and 100 Days of Hands datasets. Experiments on these datasets validate that our proposed method can achieve a notable mAP improvement on HOI detection with a faster inference speed than existing one-stage HOI detectors. Our code is available at https://github.com/scwangdyd/large_vocabulary_hoi_detection.

Large-scale canals with cascaded pools are constructed wordwide to divert water from rich to arid areas to mitigate water shortages. Efficient control of canals is essential to improve water-diversion performance. Numerous model-based approaches have been proposed and made great progress for canal control. However, when the predictive model is unavailable or unpromising for long time step predictions, model-free approaches could be considered as a possible way to achieve efficient control. Since most existing model-free approaches are focused on control of small canals or reservoirs, this article proposes a new control approach named policy and action reinforcement learning (PARL) for large-scale canals. We leverage the idea of “divide and conquer” to decompose the control task of large-scale canals into policy learning and action learning subtasks, and develop PARL by means of hierarchical reinforcement learning. Extensive experiments are conducted via numerical simulation on the case study of Chinese South to North Water Transfer Project, and experimental results show that PARL can achieve desirable performance improvements over other model-free learning approaches.

The existing deep CNN models on single image super-resolution processing are computationally-intensive in terms of memory usage and training time. In resources-limited platforms, it is desirable to consider developing light-weight models for super-resolution tasks. This paper proposes a parallel-group convolution, which uses 25% computation of the standard convolutions. With parallel-group convolutions, we develop an efficient light-weight convolutional neural network named MobileSR for super-resolution. Experimental results show that our proposed method achieves appreciable improvements over the state-of-the-art models with approximately 75% size reduction. The source code is available at https://github.com/DestinyK/MobileSR.

Delivering High-Definition (HD) real-time video to mobile devices is challenged with stringent constraints in delay and reliability. In the presence of network dynamics (e.g., channel errors and bandwidth fluctuations), the existing error-control mechanisms [e.g., Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC)] frequently induce deadline violations and quality degradations. To strike an effective balance between delay and reliability in real-time video transmission, this research presents an application-layer solution dubbed Partial rEliability based Real-timEStreaming (PERES) to perform partially reliable transfer. First, we develop an analytical framework to model the delay-constrained partial reliability for acknowledgement (ACK) and negative acknowledgement (NAK) based real-time video streaming. Second, we propose scheduling algorithms for video-aware reliability adaptation and network-adaptive buffer control. PERES is able to maximize the transmission reliability of high-priority video frames within stringent delay constraint. We implement the proposed transmission scheme in embedded video monitoring systems and evaluate the efficacy over different wireless network environments. Evaluation results demonstrate PERES achieves appreciable improvements over the reference schemes in perceptual video quality, delay performance, and bandwidth efficiency.

Prompted by technological advancements in wireless systems and handheld devices, concurrent multipath transfer is a promising solution to stream high-quality mobile videos in heterogeneous access medium. Multipath TCP (MPTCP) is a transport-layer protocol recommended by the Internet Engineering Task Force (IETF) for concurrent data transmission to multi-radio terminals. However, it is still challenging to stream high-quality real-time videos with the existing MPTCP solutions because of the tradeoff between energy efficiency and video quality. To deliver real-time video in an energy-efficient manner, this paper presents a Delay-Energy-quAlity-aware MPTCP (DEAM) solution. First, an analytical framework is developed to characterize the delay-constrained energy-quality tradeoff for multipath video delivery over heterogeneous access networks. Second, a subflow allocation algorithm is proposed to minimize the device energy consumption while achieving target video quality within the imposed deadline. The performance of the proposed DEAM is verified by means of extensive Exata emulations with real-time streaming videos. Evaluation results demonstrate that DEAM achieves appreciable improvements over the reference MPTCP solutions in mobile energy conservation and user-perceived video quality.

Video surveillance has become an important Internet multimedia application to provide live streaming services. Energy efficiency is critical to guarantee the service time and quality of power-intensive video streaming on wireless surveillance systems. In particular, video coding and data communication constitute the majority of power dissipation in embedded multimedia devices driven by capacity-limited batteries. However, the complex power characteristics and time-varying channel status pose crucial challenges on enabling low-power high-quality video streaming. To address these critical problems, this paper presents a power-efficient and network-adaptive (PENA) framework for source- forward error correction (FEC) coding in embedded systems. First, an analytical framework is developed to characterize the rate-distortion-power tradeoff for video encoding and data transfer. Second, a joint rate control and FEC coding solution is proposed to maximize video quality under power constraint. Distinct from the existing source-FEC coding algorithms, PENA is able to effectively leverage the video rate-distortion model and system power features. We conduct the system implementation and performance evaluation with real embedded surveillance devices over wireless networks. Experimental results demonstrate PENA achieves appreciable improvements over the reference source-FEC coding schemes in terms of power conservation, perceived video quality, and end-to-end delay.

The advancements in networking technologies and hand-held devices enable mobile users to concurrently receive real-time multimedia streaming (e.g., high-definition video) with different radio interfaces (e.g., Wi-Fi and LTE networks). Stream control transmission protocol (SCTP) is an important transport-layer solution to implement concurrent multipath transfer (CMT) over heterogeneous wireless networks with multihomed terminals. However, it is challenging to distribute multimedia content to resource-limited mobile devices because of the contradiction between energy consumption and streaming quality. To deliver the energy-efficient and quality-aware multimedia streaming over multiple wireless networks, this paper presents an Energy and goodPut Optimized CMT (EPOC) solution. First, we develop an analytical framework to model the relationship between energy consumption and goodput performance for real-time multimedia transmission to multihomed mobile devices. Second, we propose a joint forward error correction coding and rate allocation scheme to minimize energy consumption while satisfying goodput constraint. EPOC effectively leverages the energy-goodput tradeoff and multipath diversity to optimize mobile multimedia transmission in heterogeneous networking environment. We conduct the performance evaluation through extensive semi-physical emulations in Exata involving H.264 video streaming. Compared with the reference CMT schemes using SCTP, EPOC achieves appreciable improvements in energy conservation, goodput, and video peak signal-to-noise ratio.

Supported by the latest technical innovations mobile users are able to simultaneously receive real-time streaming services with different radio access technologies (e.g. LTE and Wi-Fi). The stream control transmission protocol (SCTP) is a vitally important transport protocol to enable concurrent multipath transfer (CMT) in heterogeneous wireless networks with multihomed terminals. However enabling CMT of real-time video streaming to multihomed mobiles is challenged with key technical dilemmas: 1) high-quality real-time video transmission is constrained by stringent requirements in delay and throughput; 2) wireless networks are bandwidth-limited and error-prone; and 3) the congestion control and packet retransmission modules in the SCTP may incur frequent deadline violations and throughput fluctuations. Motivated by addressing these challenging problems this research proposes a Video and Raptor code aware CMT (CMT-VR) solution. First we develop a mathematical model to formulate the utility maximization problem of multipath real-time video delivery over parallel wireless networks. Second we present a transmission framework that includes online packet scheduling Raptor coding adaptation and retransmission control algorithms. CMT-VR is distinct from the existing SCTPs in leveraging the video frame priority and rateless Raptor coding. The performance verification is conducted by means of system evaluations over real wireless networks and extensive semiphysical emulations in the Exata platform. Evaluation results demonstrate that CMT-VR achieves appreciable improvements over the reference schemes in perceived video quality goodput and end-to-end delay.

Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes Differentiated cloud-Assisted VIdeo Streaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.

With the plummeting cost of the next-generation sequencing technologies, high-density genetic linkage maps could be constructed in a forest hybrid F1 population. However, based on such genetic maps, quantitative trait loci (QTL) mapping cannot be directly conducted with traditional statistical methods or tools because the linkage phase and segregation pattern of molecular markers are not always fixed as in inbred lines. We implemented the traditional composite interval mapping (CIM) method to multivariate trait data in forest trees and developed the corresponding software, mvqtlcim. Our method not only incorporated the various segregations and linkage phases of molecular markers, but also applied Takeuchi’s information criterion (TIC) to discriminate the QTL segregation type among several possible alternatives. QTL mapping was performed in a hybrid F1 population of Populus deltoides and P. simonii, and 12 QTLs were detected for tree height over 6 time points. The software package allowed many options for parameters as well as parallel computing for permutation tests. The features of the software were demonstrated with the real data analysis and a large number of Monte Carlo simulations. We provided a powerful tool for QTL mapping of multiple or longitudinal traits in an outbred F1 population, in which the traditional software for QTL mapping cannot be used. This tool will facilitate studying of QTL mapping and thus will accelerate molecular breeding programs especially in forest trees. The tool package is freely available from https://github.com/tongchf /mvqtlcim.

The spectrum limitation of single wireless networks prompts the bandwidth aggregation of heterogeneous access medium (e.g., LTE and Wi-Fi) to support high-quality real-time video services. Energy consumption of mobile devices is of vital significance to provide user-satisfied multimedia streaming applications. However, it is challenging to develop an energy-efficient bandwidth aggregation scheme with regard to the stringent delay and quality constraints imposed by wireless video transmission. To address the critical problem, this paper presents an Energy-quaLity aware Bandwidth Aggregation (ELBA) scheme. First, we develop an analytical framework to model the delay-constrained energy-quality tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a bandwidth aggregation framework that integrates energy-minimized rate adaptation, delay-constrained unequal protection, and quality-aware packet distribution. The proposed ELBA scheme is able to effectively leverage the wireless channel diversity and video frame priority for enabling energy-minimized quality-guaranteed streaming to multihomed devices within imposed deadline. We conduct the performance evaluation through both experiments over real wireless networks and extensive emulations in Exata platform. Experimental results demonstrate the performance advantages of ELBA over existing bandwidth aggregation schemes in energy conservation, video quality, and end-to-end delay.

Cloud gaming has emerged as a promising application to enable high-end game playing with thin clients. Transmission control protocol (TCP) is pervasively adopted as the transport-layer protocol in the mainstream cloud gaming systems for video communication. However, streaming mobile cloud gaming video using the TCP is challenged with several key technical barriers: (1) the performance limitations of wireless networks in bandwidth and reliability; (2) the high throughput demand and stringent delay constraint imposed by high-quality gaming video transmission; and (3) the deadline violations and throughput fluctuations caused by the packet retransmission and congestion control mechanisms in the TCP. To address these critical problems, this paper proposes an application-layer source-forward error correction (FEC) coding framework dubbed adaptive source-FEC coding over TCP (ESCOT). First, we analytically formulate the optimization problem of joint source-FEC coding to minimize the end-to-end distortion of real-time video communication over TCP. Second, we develop a heuristic solution for effective loss rate approximation, source rate control, and FEC coding adaptation. ESCOT is distinct from existing source-FEC coding schemes in proactively analyzing and leveraging the TCP characteristics. The proposed solution is able to effectively mitigate both consecutive and sporadic video frame drops caused by congestion and random packet losses. We conduct the performance evaluation through extensive emulations in the Exata platform using real-time gaming video encoded by the H.264 codec. Experimental results show that the ESCOT advances the state of the art with noticeable improvements in video peak signal-to-noise ratio, end-to-end delay, goodput, and frame success rate.

High-Definition (HD) wireless video has already dominated popular multimedia applications to provide content-rich mobile streaming services. Transmission Control Protocol (TCP) is pervasively employed as the transport-layer solution in video communication systems to achieve firewall traversal and network-friendliness. However, it is severely challenging to effectively deliver HD mobile video using TCP over wireless networks: 1) HD live video streaming is featured by high transmission rate and stringent delay constraint; 2) wireless networks are bandwidth-limited and error-prone; and 3) the congestion control and data retransmission mechanisms in TCP may result in frequent throughput fluctuations and deadline violations. To address these critical issues, this research presents a Forward Error Correction (FEC) coding scheme dubbed PATON (Priority-Aware and TCP-Oriented codiNg). First, we develop an analytical model of FEC coding-based real-time video communication with TCP over wireless networks. Second, we propose a heuristic solution for prioritized frame selection, FEC redundancy adaptation, and packet size adjustment to maximize real-time video quality. The proposed PATON is able to effectively leverage the frame priority and TCP connection state to minimize the video distortion. We conduct the performance evaluation through extensive semi-physical emulations in Exata involving HD video streaming encoded with H.264 codec. Compared with the existing FEC coding schemes, PATON achieves appreciable improvements in terms of video Peak Signal-to-Noise Ratio (PSNR), end-to-end delay, ratio of lost frames, and goodput.

The advancements in wireless communication technologies prompt the bandwidth aggregation for mobile video delivery over heterogeneous access networks. Multipath TCP (MPTCP) is the transport protocol recommended by IETF for concurrent data transmission to multihomed terminals. However, it still remains challenging to deliver user-satisfied video services with the existing MPTCP schemes because of the contradiction between energy consumption and received video quality in mobile devices. To enable the energy-efficient and quality-guaranteed video streaming, this paper presents an energy-distortion-aware MPTCP (EDAM) solution. First, we develop an analytical framework to characterize the energy-distortion tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a video flow rate allocation algorithm to minimize the energy consumption while achieving target video quality based on utility maximization theory. The performance of the proposed EDAM is evaluated through both experiments in real wireless networks and extensive emulations in exata. Experimental results show that EDAM exhibits performance advantages over existing MPTCP schemes in energy conservation and video quality.

The rapid advancements in networking technologies and hand-held devices enable mobile users to concurrently receive real-time multimedia streaming (e.g., high-definition video) with different radio interfaces (e.g., Wi-Fi and LTE networks). Stream control transmission protocol (SCTP) is an important transport-layer solution to implement concurrent multipath transfer (CMT) over heterogeneous wireless networks with multihomed terminals. However, it is challenging to distribute multimedia content to the resource-limited mobile devices because of the contradiction between energy consumption and streaming quality. To deliver the energy-efficient and quality-aware multimedia streaming over multiple wireless networks, this paper presents an Energy and goodPut Optimized CMT (EPOC) solution. First, we develop an analytical framework to model the relationship between energy consumption and goodput performance for real-time multimedia transmission to multihomed mobile devices. Second, we propose a joint Forward Error Correction (FEC) coding and rate allocation scheme to minimize energy consumption while satisfying goodput constraint. EPOC effectively leverages the energy-goodput tradeoff and multipath diversity to optimize mobile multimedia transmission in heterogeneous networking environment. We conduct the performance evaluation through extensive semi-physical emulations in Exata involving H.264 video streaming. Compared with the reference CMT schemes using SCTP, EPOC achieves appreciable improvements in energy conservation, goodput and Peak Signal-to-Noise Ratio (PSNR).

The advancements in wireless infrastructures and communication technologies prompt the bandwidth aggregation for mobile video delivery over heterogeneous access networks. Multipath TCP (MPTCP) is the transport protocol recommended by IETF to enable concurrent data transmissions over multiple communication paths. However, it still remains problematic to deliver user-satisfied video services with the existing MPTCP schemes due to the contradiction between mobile device energy and video distortion. To enable the energy-efficient and quality-guaranteed video streaming, this paper presents an Energy-Distortion Aware MPTCP (EDAM) solution. First, we develop an analytical model to capture the energy-distortion tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a video flow rate allocation algorithm to minimize the energy consumption while achieving target video quality based on utility maximization theory. The performance of the proposed EDAM is evaluated through extensive emulations in Exata involving real-time H.264 video streaming. Evaluation results demonstrate that EDAM outperforms the reference MPTCP schemes in reducing energy consumption, as well as in improving video PSNR (Peak Signal-to-Noise Ratio).

The technological evolutions in wireless communication systems prompt the bandwidth aggregation (e.g., Wi-Fi and LTE radio interfaces) for concurrent video transmission to hand-held devices. However, multipath video transport to the battery-limited mobile terminals is confronted with challenging technical problems: 1) high-quality real-time video streaming is throughput-demanding and delay-sensitive; 2) mobile device energy and video quality are not adequately considered in conventional multipath protocols; and 3) wireless networks are error-prone and bandwidth-limited. To enable the energy-efficient and quality-guaranteed live video streaming over heterogeneous wireless access networks, this paper proposes an energy-video aware multipath transport protocol (EVIS). First, we present a mathematical framework to analyze the frame-level energy-quality tradeoff for delay-constrained multihomed video communication over multiple communication paths. Second, we develop scheduling algorithms for prioritized frame scheduling and unequal loss protection to achieve target video quality with minimum device energy consumption. EVIS is able to effectively leverage video frame priority and rateless Raptor coding to jointly optimize energy efficiency and perceived quality. We conduct performance evaluation through extensive emulations in Exata involving real-time H.264 video streaming. Emulation results demonstrate that EVIS advances the state-of-the-art with remarkable improvements in energy conservation, video peak signal-to-noise ratio (PSNR), end-to-end delay, and goodput.

High-frame-rate (HFR) video technology is becoming widely implemented in popular multimedia applications (e.g., Youtube and cloud gaming) to provide a smooth viewing experience, while transmission control protocol (TCP) is pervasively adopted as the transport-layer solution for video communications to achieve firewall traversal and network friendliness. However, it is severely challenging to effectively deliver real-time HFR video over TCP: 1) HFR video streaming features high transmission rate, enhanced frame density, and stringent delay constraint and 2) the packet retransmission and congestion control mechanisms in TCP may cause frequent throughput fluctuations and deadline violations. Motivated by addressing these critical issues, this research presents an application-layer forward error correction (FEC) framework dubbed Raptor coded HFR video over TCP (ROCHET). First, we develop a mathematical model to analyze the frame-level distortion of systematic Raptor code-based HFR video communication over TCP in wireless networks. Second, we propose a joint approximate distortion estimation and Raptor coding adaption solution to minimize the sum of total distortion. The proposed ROCHET is able to effectively leverage unequal error protection and TCP state analysis to enhance streaming video quality. We conduct the performance evaluation through extensive emulations in Exata involving real-time HFR video encoded with H.264 codec. Compared with the existing FEC coding schemes, ROCHET achieves appreciable improvements in terms of video peak signal-to-noise ratio, goodput, and frame success rate. Thus, ROCHET is recommended for TCP-based HFR video transmission over wireless networks.

High-frame rate (HFR) video streaming is emerging as a new paradigm in popular multimedia applications (e.g., mobile cloud gaming) to achieve smooth viewing experience perceived by end users. However, it is severely challenging to guarantee the delivery quality of HFR video over wireless platforms with regard to the high transmission rate and limited network resources. Multihoming capability enables mobile devices to concurrently receive video data with different radio interfaces (e.g., cellular and Wi-Fi). To effectively deliver mobile HFR video over multiple wireless access networks, this paper develops an application-layer transmission scheme dubbed joint FRAme Scheduling and Error Resilience (FRASER). First, we propose an unequal frame scheduling approach by taking advantage of interlaced forward error correction coding and reliability-aware data allocation to minimize the total distortion. Second, we introduce an error resilience scheme at the receiver side to proactively leverage the out-of-order and overdue video packets to mitigate the error propagations. The proposed FRASER is able to substantially reduce the probability of I (Intra) frame loss and consecutive P (Predicted) frame drops. We conduct the performance evaluation through extensive emulations in Exata involving HFR video streaming encoded with H.264 codec. Evaluation results show that FRASER outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and received frame rate. Thus, FRASER is recommended for delivering HFR video streaming to multihomed mobile devices in heterogeneous wireless networks.

Recent technological advancements in wireless infrastructures and handheld devices enable mobile users to concurrently receive multimedia contents with different radio interfaces (e.g., cellular and Wi-Fi). However, multipath video transport over the resource-limited and error-prone wireless networks is challenged with key technical issues: 1) conventional multipath protocols are throughput-oriented, and video data are scheduled in a content-agnostic fashion and 2) high-quality real-time video is bandwidth-intensive and delay-sensitive. To address these critical problems, this paper proposes a bandwidth-efficient multipath streaming (BEMA) protocol featured by the priority-aware data scheduling and forward error correction-based reliable transmission. First, we present a mathematical framework to formulate the delay-constrained distortion minimization problem for concurrent video transmission over multiple wireless access networks. Second, we develop a joint Raptor coding and data distribution framework to achieve target video quality with minimum bandwidth consumption. The proposed BEMA is able to effectively mitigate packet reordering and path asymmetry to improve network utilization. We conduct performance evaluation through extensive emulations in Exata involving real-time H.264 video streaming. Compared with the existing multipath protocols, BEMA achieves appreciable improvements in terms of video peak signal-to-noise ratio, end-to-end delay, bandwidth utilization, and goodput. Therefore, BEMA is recommended for streaming high-quality real-time video to multihomed terminals in heterogeneous wireless networks.

We study the problem of one-way video communication in a single-source, multiple-destination scenario over the lossy Internet. Forward error correction (FEC) coding is commonly adopted for data protection in implementing loss-resilient video transmission systems. However, the burst packet losses over the Internet frequently degrade FEC performance and induce video quality deteriorations. To address the challenging problem, we propose a novel transmission scheme dubbed trading delay for distortion (TELFORD) that includes three components: 1) adaptive multidestination status estimation; 2) delay-constrained transmission rate assignment; and 3) differentiated FEC packet spreading. We analytically formulate and solve the problem of FEC packet allocation and scheduling to minimize the end-to-end video distortion. The proposed TELFORD is able to cope with multiple-destination scheduling separately. We conduct performance evaluation through semiphysical emulations in Exata using real-time H.264 video streaming. Experimental results show that TELFORD outperforms existing error control transmission schemes in improving the video peak signal-to-noise ratio and mitigating packet transmission impairments.

Delivering high-quality mobile video with the limited radio resources is challenging due to the time-varying channel status and stringent Quality of Service (QoS) requirements. Multi-homing support enables mobile terminals to establish multiple simultaneous associations for enhancing transmission performance. In this paper, we study the multi-homed communication of delay-constrained High Definition (HD) video in heterogeneous wireless networks. The low-delay encoded HD video streaming consists exclusively of Intra (I) and Predicted (P) frames. In the capacity-limited wireless networks, it is highly possible the large-size I frames experience deadline violations and induce severe quality degradations. To address the challenging problem, we propose a novel scheduling framework dubbed delAy Stringent COded Transmission (ASCOT) that featured by frame-level data protection and allocation overmultiple wireless access networks. First, we perform online video distortion estimation to simulate multi-homed transmission impairments according to the feedback channel status and input video data. Second, we control the frame protection level by adapting the Forward Error Correction (FEC) coding redundancy and video data allocation to achieve target quality. The performance of the proposed ASCOT is evaluated through semi-physical emulations in Exata using real-time H.264 video streaming. Experimental results show that ASCOT outperforms existing transmission schemes in improving the video PSNR (Peak Signal-to-Noise Ratio), reducing the end-to-end delay, and increasing the goodput. Or conversely, ASCOTachieves the same video quality with approximately 20 percent bandwidth conservation.

The proliferating wireless infrastructures with complementary characteristics prompt the bandwidth aggregation for concurrent video transmission in heterogeneous access networks. Multipath TCP (MPTCP) is an important transport-layer protocol recommended by IETF to integrate different access medium (e.g., Cellular and Wi-Fi). This paper investigates the problem of mobile video delivery using MPTCP in heterogeneous wireless networks with multihomed terminals. To achieve the optimal quality of real-time video streaming, we have to seriously consider the path asymmetry in different access networks and the disadvantages of the data retransmission mechanism in MPTCP. Motivated by addressing these critical issues, this study presents a novel quAlity-Driven MultIpath TCP (ADMIT) scheme that integrates the utility maximization based Forward Error Correction (FEC) coding and rate allocation. We develop an analytical framework to model the MPTCP-based video delivery quality over multiple communication paths. ADMIT is able to effectively integrate the most reliable access networks with FEC coding to minimize the end-to-end video distortion. The performance of ADMIT is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that ADMIToutperforms the reference transport protocols in terms of video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and goodput. Thus, we recommend ADMIT for streaming high-quality mobile video in heterogeneous wireless networks with multihomed terminals.

Delivering high-definition (HD) wireless video under stringent delay constraint is challenging with regard to the limited network resources and high transmission rate. Concurrent multipath transfer (CMT) using stream control transmission protocol (SCTP) exploits the multihoming feature of mobile devices to establish associations with different access networks. In this paper, we study the multihomed HD video communication with SCTP over heterogeneous wireless networks. The existing CMT schemes mainly treat the traffic data in a content-agnostic fashion, and thus cannot effectively leverage the scarce wireless resources to maximize the perceived video quality. To address this critical issue, we propose a content-aware CMT (CMT-CA) solution that featured by the unequal frame-level scheduling based on estimated video parameters and feedback channel status. First, we develop an analytical framework to model the total distortion of parallel video transmission over multiple wireless access networks. Second, we introduce a joint congestion control and data distribution scheme to minimize the total distortion based on online quality evaluation and Markov decision process (MDP). The performance of CMT-CA is evaluated through extensive semi-physical emulations in Exata involving HD video encoded with H.264 codec. Experimental results show that CMT-CA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, and goodput. Or conversely, CMT-CA achieves the same video quality with 20 percent bandwidth conservation.

High frame rate (HFR) video is emerging as a new paradigm in popular multimedia applications (e.g., cloud gaming) to achieve smooth viewing experience perceived by end-users. In the context of HFR streaming video, end-to-end distortion and sending frame rate are equally important to the perceptual quality. This study presents a modeling-based approach to optimizing the HFR video transmission over wireless networks. First, we develop an analytical model dubbed FRIED (Frame Rate versus vIdEo Distortion) to characterize the tradeoff between sending frame rate and end-to-end video distortion. Second, we propose a Joint frAme Selection and FEC (Forward Error Correction) cOding (JASCO) approach based on the FRIED model to optimize the transmission performance. The efficacy of the proposed JASCO is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that JASCO outperforms the reference approaches in improving video peak signal-to-noise ratio (PSNR) at the same frame rate. Or conversely, JASCO is able to achieve higher received frame rate while guaranteeing the same video PSNR.

Inter destination video synchronisation (IDVS) is a key technology in emerging interactive multimedia applications. It is essential to ensure the synchronous experiences of users in such applications. However, one inevitable barrier for IDVS is the packet transfer delay differences (PTDDs) among different destinations. Existing researches have tried to eliminate the side effects of PTDD passively and schedule the video packets in a ‘back‐to‐back’ fashion. In this paper, the authors propose to proactively leverage such differences to design a transmission scheme that enhances video quality while ensuring the synchronous arrival of packets. Based on the network measurements from a real multimedia conferencing system and the Planetlab, they find that the PTDD is between the ranges of 100–250 ms. Motivated by this observation, they propose a scheme named asynchronous departure for synchronous arrival (ADSA), which inserts intervals between consecutive packets according to the synchronisation reference. To prove the superiority of ADSA, they carry out analysis based on Gilbert loss model and continuous time Markov chain. They conduct performance evaluation through emulations in Exata and experimental results show that ADSA improves the video peak signal‐to‐noise ratio by up to 9.1 and 6.9 dB compared with existing latest and earliest schemes, respectively.

Transmission control protocol (TCP) is pervasively employed as the transport-layer solution in popular video applications (e.g., Skype, Google+, HTTP-based adaptive video streaming, etc.) for firewall traversal and network-friendliness. To remedy the shortcomings of the data retransmission mechanism in TCP, forward error correction (FEC) coding is commonly used as the application-layer error-resilient scheme in live media streaming systems. An important measurement study reveals that TCP exhibits delay-friendliness (i.e., delay-performance bias) towards traffic flows composed of small-size packets. Motivated by leveraging the delay-friendliness of TCP to optimize the real-time streaming video quality, we propose a novel FEC coding scheme dubbed Coded Live vide OStreaming ov Er TCP (CLOSET). To achieve the optimal video quality over the lossy communication networks, we analytically formulate the constrained optimization problem of joint FEC coding and packet interleaving to minimize the effective packet loss rate. Then, we provide an approximate analysis to derive the solution for online adaption of FEC redundancy, packet size, and interleaving level. The performance of CLOSET is evaluated through extensive semi-physical emulations in Exata involving real-time encoded H.264 video streaming. Experimental results show that CLOSET outperforms the reference FEC coding schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, ratio of overdue video frames, and goodput. Therefore, we recommend CLOSET for TCP-based real-time video communication systems.

High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems.

The massive proliferation of wireless infrastructures with complementary characteristics prompts the bandwidth aggregation for Concurrent Multipath Transfer (CMT) over heterogeneous access networks. Stream Control Transmission Protocol (SCTP) is the standard transport-layer solution to enable CMT in multihomed communication environments. However, delivering high-quality streaming video with the existing CMT solutions still remains problematic due to the stringent quality of service (QoS) requirements and path asymmetry in heterogeneous wireless networks. In this paper, we advance the state of the art by introducing video distortion into the decision process of multipath data transfer. The proposed distortion-aware concurrent multipath transfer (CMT-DA) solution includes three phases: 1) per-path status estimation and congestion control; 2) quality-optimal video flow rate allocation; 3) delay and loss controlled data retransmission. The term `flow rate allocation' indicates dynamically picking appropriate access networks and assigning the transmission rates. We analytically formulate the data distribution over multiple communication paths to minimize the end-to-end video distortion and derive the solution based on the utility maximization theory. The performance of the proposed CMT-DA is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that CMT-DA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), goodput, and inter-packet delay.

Load distribution is a key research issue in deploying the limited network resources available to support traffic transmissions. Developing an effective solution is critical for enhancing traffic performance and network utilization. In this paper, we investigate the problem of load distribution for real-time traffic over multipath networks. Due to the path diversity and unreliability in heterogeneous overlay networks, large end-to-end delay and consecutive packet losses can significantly degrade the traffic flow's goodput, whereas existing studies mainlyfocus on the delay or throughput performance. To address the challenging problems, we propose a Goodput-Aware Load distribuTiON (GALTON) model that includes three phases: (1) path status estimation to accurately sense the quality of each transport link, (2) flow rate assignment to optimize the aggregate goodput of input traffic, and (3) deadline-constrained packet interleaving to mitigate consecutive losses. We present a mathematical formulation for multipath load distribution and derive the solution based on utility theory. The performance of the proposed model is evaluated through semi-physical emulations in Exata involving both real Internet traffic traces and H.264 video streaming. Experimental results show that GALTON outperforms existing traffic distribution models in terms of goodput, video Peak Signal-to-Noise Ratio (PSNR), end-to-end delay, and aggregate loss rate.

Bandwidth aggregation is a key research issue in integrating heterogeneous wireless networks, since it can substantially increase the throughput and reliability for enhancing streaming video quality. However, the burst loss in the unreliable wireless channels is a severely challenging problem which significantly degrades the effectiveness of bandwidth aggregation. Previous studies attack such a critical problem by reactively increasing the Forward Error Correction (FEC) redundancy. Nonetheless, the increased FEC redundancy will impose higher loads on networks and lead to larger end-to-end delays of video streaming. In this paper, we propose a novel Loss Tolerant Bandwidth Aggregation approach (LTBA), which proactively leverages the channel diversity in heterogeneous wireless networks to overcome the burst loss. First, we allocate the FEC packets according to the ‘loss-free’ bandwidth of each wireless network to the multihomed client. Second, we deliberately insert intervals between the FEC packets' departures while still respecting the delay constraint. The proposed LTBA is able to reduce the consecutive packet loss under the burst loss assumption. We carry out the analysis for proving that the proposed LTBA outperforms the existing ‘back-to-back’ transmission schemes in minimizing end-to-end video distortion based on the Gilbert path model and continuous time Markov chain. We conduct the performance evaluation in the Exata and emulation results show that LTBA outperforms existing bandwidth aggregation schemes in handling the burst channel loss as well as in improving the video PSNR (Peak Signal-to-Noise Ratio).

We consider the problem of high definition video transmission in a heterogeneous wireless network from a video server to a multihomed client. On one hand, a single wireless network is limited in transmission performance (e.g., available bandwidth and link delay); On the other hand, the high definition video streaming is characterized by high transmission rate and large-size video frames. Thus, the end-to-end video frame delay becomes a severely challenging problem which is critical for real-time video applications. In this paper, we propose a novel scheduling approach named SFL, which deliberately splits large-size video frames into sub-frames and dispatches each of them onto a different wireless network to the multihomed client. This approach is able to improve the frame-level delay performance for enhancing video quality. We formulate a optimization problem of video streaming allocation for minimizing the end-to-end delay based on the network calculus and derive its solution with the water filling algorithm. We evaluate the performance of the proposed SFL through Exata emulations using real-time H.264 video streaming. Emulation results show that SFL outperforms existing frame level scheduling approaches in improving frame-level delay as well as in enhancing video quality in terms of PSNR (Peak Signal-to-Noise Ratio).

Load distribution is vital to the performance of multipath transport. The task becomes more challenging in real-time multimedia applications (RTMA), which impose stringent delay requirements. Two key issues to be addressed are: 1) how to minimize end-to-end delay, and 2) how to alleviate packet reordering that incurs additional recovery time at the receiver. In this paper, we propose SPMLD, a new model that splits traffic at the granularity of sub-packet. The Sub-Packet based Multipath Load Distribution (SPMLD) model aims to minimize total packet delay by effectively aggregating multiple parallel paths as a single virtual path. First, we formulate the packet splitting over multiple paths as a constrained optimization problem and derive its solution based on progressive approximation method. Second, in the solution, we analyze queuing delay by introducing D/M/1 model and obtain the expression of dynamic packet splitting ratio for each path. The performances of SPMLD are evaluated through extensive simulations in QualNet using real-time H.264 video streaming. Experimental results demonstrate that: SPMLD outperforms previous flow and packet based load distribution models in total packet delay, end-to-end delay and seldom induces packet reordering. Besides, SPMLD's extra overhead is tiny compared to the input video streaming.

The safety production in coal mine has attracted considerable research attentions due to the frequently occurred mining accidents. In order to ensure the safety production in coal mine, technology of the Internet of Things (IoT) is widely used to detect the situation in coal mine. Here the situation is composed of several elements. Existing solutions for such situation detection are mainly based on the directed graph or automatic machine. These methods are only effective when few situation element change simultaneously or the change(s) can be determined clearly. However, when the situation comprises a lot of elements or the element's change is ambiguous, these methods cannot effectively determine the situation. In this paper, we propose a situation detection method based on neural network. Trained neural network can detect the situation well, especially when multiple situation elements change at the same time or changes are ambiguous.