Mobile video chatting has emerged as an important Internet multimedia application that greatly enriches interpersonal communications. Mobile power efficiency is crucial to the service quality and time of video chatting on battery-limited smartphones. However, the power characteristics of the video coding and data communication are highly complex due to the time-varying network conditions and dynamic mobile energy features. This incurs crucial challenges to maintaining the low power dissipation of mobile chatting application while streaming satisfactory-quality videos. To address these challenges, this paper presents a joinT cOding-tranSmission Optimization (TOSO) protocol at application layer that performs machine learning based adaptation of the video bit rate and FEC (Forward Error Correction) coding parameters. By taking advantage of analytical and empirical models characterizing the quality-power relationship, TOSO is able to maximize video quality subject to a specified upper bound of power consumption in mobile chat application. This distinguishing feature prevents the video chat from draining battery too quickly. Moreover, it allows the smartphone operating system or the mobile user to define a desired video chat duration given the remaining battery, avoiding unpleasant conversation disruption due to battery depletion. Extensive experiments based on the Linphone platform and Exata network emulator show that TOSO outperforms baseline approaches by 29.3 percent in power conservation while achieving the same video quality level.

Context-aware QoS management is a new research field dealing with methods by which traditional QoS management algorithms in mobile and fixed networking systems can have more intelligent decision-making mechanisms by fully exploiting all context information being available in their network environment. Motivated by addressing the critical problem, we propose a novel context-aware cognitive QoS management approach, which contains three main steps for context discretization, context reduction and QoS guarantee, which can provide the quantitative end-to-end QoS guarantee and management. We developed a context-aware systematic end-to-end QoS management module in a real-life multimedia conferencing system and compared its performance with existing approaches. Experimental results show that our proposed approach outperforms the existing approaches in improving the mobile and fixed networking video transmission quality.

Nowadays Telecom service providers are seeking new paradigms of service creation and execution platform to reduce new services' time to market and increase profitability. However, the existing static services orchestration approaches cannot meet the dynamic complicated business demands. This paper proposes a hybrid service orchestration platform for Internet-Telephony networks. Firstly, designs a hybrid service orchestration language for developers to achieve dynamic and rapid orchestration of new hybrid services over the Internet-Telephony networks. Secondly, proposes an event-driven and component-based hybrid service orchestration container to meet the asynchronous dynamic interactions between hybrid services. Thirdly, proposes a cost-aware auto-scaling approach, including the pre-scaling and real-time scaling stages, to dynamically scale the required resources at different levels. Finally, illustrates the hybrid voice chatting services orchestration scenario, and also the effectiveness and practicability of the proposed platform are validated through extensive experiments.

Video surveillance is an important application of Internet of Thing (IoT) that provides convenience remote monitoring service to end users. How to reduce the power consumption of mobile devices with limited energy has become a research hotspot. We propose an energy-efficient architecture that includes smartphones' various power saving solutions for video transmission over wireless networks. Results demonstrate that each of our proposed solutions is significantly outperforms than the standard scheme.

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.

With the advances in wireless communication and the growing popularity of mobile devices, it has become rather normal to watch videos using mobile devices. However, there are severely challenges to using video codec on mobile devices, because: 1) insufficient computing resources, there is poor performance using mobile device ; 2) the limited battery capacity on mobile device; 3) CPU utilization is too high when using traditional video codec. In this paper, we proposed a GPU based High Definition Parallel Video Codec on mobile devices, which is an efficient video codec with the cooperation of CPU and GPU. The video codec system is fully compliant with the video codec h264 standard. Compared with the scheme using existing X264, the presented experimental results evaluated the GPU based video codec achieves appreciable improvements in FPS(frames per second), the energy consumption and utilization of CPU are reduced properly at the same time.

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.

Surveillance video has become an important multi-media application in recent years, with development trends toward wide viewing angles and high definition. At the same time, transmission of surveillance video over the Internet and storage in the cloud are becoming increasingly popular. However, it is extremely challenging to transfer surveillance video with both a wide viewing angle and a high resolution over the Internet because of 1) the tradeoff between a wide viewing angle and high-surveillance object resolution for a single camera, 2) the large throughput requirement, and 3) the fluctuating delay and bandwidth of the Internet. In this paper, we present a reference-frame-cache-based surveillance video transmission system (RSVTS), which delivers wide-viewing-angle and high-definition surveillance video over the Internet in real time using multiple rotatable cameras. First, we develop an efficient video mosaicing method for merging two (or more) surveillance videos together. Second, novel reference frame selection and update algorithms are proposed for the RSVTS encoder to reduce the amount of encoded data. Third, we implement a reference frame cache on both the sender and receiver sides to increase video quality by increasing the probability of video decoding. We conduct a performance evaluation using a simulation system that integrates RSVTS and ns-3. Compared with the H.264/SVC and H.264/AVC schemes, RSVTS achieves appreciable improvements in enhancing the video peak signal-to-noise ratio and bandwidth conservation.

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.

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.

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 Internet of Things (IoT) infrastructure with numerous diverse physical devices are growing up rapidly, which need a dynamic services coordination approach that can integrate those heterogeneous physical devices into the context-aware IoT infrastructure. This paper proposes a situation-aware dynamic IoT services coordination approach. First, focusing on the definition of formal situation event pattern with event selection and consumption strategy, an automaton-based situational event detection algorithm is proposed. Second, the enhanced event-condition-action is used to coordinate the IoT services effectively, and also the collaboration process decomposing algorithm and the rule mismatch detection algorithms are proposed. Third, the typical scenarios of IoT services coordination for smart surgery process are also illustrated and the measurement and analysis of the platform's performance are reported. Finally, the conclusions and future works are given.

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.

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.