Performance Of Video Transmission Research Articles

Spatial-frequency HEVC multiple description video coding with adaptive perceptual redundancy allocation

Multiple description coding (MDC) approaches improve the error-resilient performance of video transmission by introducing redundancy. The existing multiple description video coding (MDVC) schemes are rarely designed for particular coding structure of HEVC, and the characteristics of the human visual system (HVS) are seldom considered. In this paper, a spatial-frequency multiple description video coding with adaptive perceptual redundancy allocation framework, named SF-PMDVC, is proposed for HEVC. For descriptions generation, after polyphase down-sampling in spatial domain, a transformation based on integer discrete cosine transform (DCT) is expended to adapt to the flexible coding unit partitioning process in HEVC, and the frequency coefficients are segmented and mapped to reduce the bitrate of each description. To further improve the performance of MDVC, an adaptive perceptual redundancy allocation strategy based on visual saliency is proposed, which improves the coding efficiency adapting to the visual perception. Experimental results show that the proposed scheme improves the error resiliency of HEVC by achieving superior objective and subjective reconstructed video quality as compared to the state-of-the-art MDVC methods for HEVC.

Soft transmission of 3D video for low power and low complexity scenario

Thanks to the rapid development of naked-eye 3D and wireless communication technology, 3D video related applications on mobile devices have attracted a lot of attention. Nevertheless, the time-varying characteristics of the wireless channel is very challenging for conventional source-channel coding based transmission strategy. Also, the high complexity of source-channel coding based transmission scheme is undesired for low power mobile terminals. An advanced transmission scheme named Softcast was proposed to achieve efficient transmission performance for 2D image/video. Unfortunately, it cannot be directly applied to wireless 3D video transmission with high efficiency. This paper proposes a more efficient soft transmission scheme for 3D video with a graceful quality adaptation within a wide range of channel Signal-to-Noise Ratio (SNR). The proposed method first extends the linear transform to 4 dimensions with additional view dimension to eliminate the view redundancy, and then metadata optimization and chunk interleaving are designed to further improve the transmission performance. Meanwhile, a synthesis distortion based chunk discard strategy is developed to improve the overall 3D video quality under the condition of limited bandwidth. The experimental results demonstrate that the proposed method significantly improves the 3D video transmission performance over the wireless channel for low power and low complexity scenarios.

Optimizing Video Transmission Performance in 5ghz Manet

Nowadays, one of the most important technologies is video transmission. It is used worldwide across multiple platforms such as mobile, tablets, smart watches, and many more. All these devices use the 2.4GHz and 5GHz frequency ranges of IEEE 802.11. Due to the mobility of these devices, it’s very important to optimize video transmission streaming between these mobile devices with minimum delay and higher throughput. In this paper, an attempt will be made to enhance the transmission performance of video over MANET using IEEE 802.11n 5GHz frequency range supported by the new devices. Theperformance of two famous routing protocols, AODV and OLSR, is compared in terms of delay, throughput, and retransmission attempts.

LensCast: Robust Wireless Video Transmission Over MmWave MIMO With Lens Antenna Array

In this paper, we present LensCast, a novel cross-layer video transmission framework for wireless networks, which seamlessly integrates millimeter wave (mmWave) lens multiple-input multiple-output (MIMO) with robust video transmission. LensCast is designed to exploit the video content diversity at the application layer, together with the spatial path diversity of lens antenna array at the physical layer, to achieve graceful video transmission performance under varying channel conditions. In LensCast, a transmission distortion minimization problem is formulated with the consideration of video chunk scheduling, path matching and power allocation, which is an intractable mixed integer non-linear programming (MINLP) problem. The solution of this MINLP problem is converted into resource allocation (i.e., joint path matching and power allocation) plus chunk scheduling. First, resource allocation is investigated with given chunk scheduling results. By analyzing the optimality of the resource allocation problem, a winner-takes-all assignment is obtained to guide resource allocation. After that, a greedy water-filling algorithm is proposed as a near-optimal solution. Second, we propose a low-complexity chunk scheduling algorithm to schedule chunks for each transmission. Simulation results demonstrate that the proposed LensCast achieves an improved performance in terms of both peak signal-to-noise ratio and visual quality comparing with reference schemes.

Reinforcement learning based energy-efficient internet-of-things video transmission

The video transmission in the Internet-of-Things (IoT) system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services. In this paper, we propose a reinforcement learning based energy-efficient IoT video transmission scheme that protects against interference, in which the base station controls the transmission action of the IoT device including the encoding rate, the modulation and coding scheme, and the transmit power. A reinforcement learning algorithm state-action-reward-state-action is applied to choose the transmission action based on the observed state (the queue length of the buffer, the channel gain, the previous bit error rate, and the previous packet loss rate) without knowledge of the transmission channel model at the transmitter and the receiver. We also propose a deep reinforcement learning based energy-efficient IoT video transmission scheme that uses a deep neural network to approximate Q value to further accelerate the learning process involved in choosing the optimal transmission action and improve the video transmission performance. Moreover, both the performance bounds of the proposed schemes and the computational complexity are theoretically derived. Simulation results show that the proposed schemes can increase the peak signal-to-noise ratio and decrease the packet loss rate, the delay, and the energy consumption relative to the benchmark scheme.

Various layer techniques to improve video transmission on 20 GHz radio link in a rainy environment

The transmission rate via radio increases to transmitting frequency increment. The higher frequency the higher transmission rate. Video traffics require radio with large bandwidth. This requirement may be fulfilled by the use of millimetre radio transmission on 20 GHz. However, radio signal on millimetre band decreases significantly in presence of rain mainly caused by link attenuation. This paper evaluates the performance of video transmission on 20 GHz in presence of rain on tropical environment. As various layers techniques are available, some techniques such as APA, Java Servlet Alias (JSPA), Digital Subtraction Angiography (DSA) and mac-application layer scheduling are implemented to help radio dealing with rain attenuation. The evaluation results through designed simulations proves that the video quality degrades significantly to rain. The combined technique is able to increase video quality, namely; physical technique up to 0.395 dB, MAC scheduler up to 6.5dB and upper layer technique achieves 1.17 dB.

A multi-hop routing protocol for video transmission in IoVs based on cellular attractor selection

Video transmission in Internet of Vehicles (IoVs) is an emerging technology which utilizes the multimedia inside and outside vehicles itself through Vehicle Ad Hoc Networks (VANETs). In IoVs, traditional multi-hop routing protocols are not adaptive to mobile environment, especially the high-mobility driving environment in which vehicles need to accomplish video transmission under high quality of service (QoS). In this paper, we propose a multi-hop routing protocol for video transmission in IoVs based on cellular attractor selection (MRVT-CAS). We design a packet generation method for MRVT-CAS and use Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to construct the candidate set of next-hop selection. Then we map the expression of different genes in cell to selection of different next-hop nodes, and employ the mechanics of cellular attractor selection to select next-hop node. Moreover, we present a real-time feedback process to improve self-adaptability and robustness of routing protocol. Our simulation study compares MRVT-CAS with other routing protocols to evaluate performance of video transmission. The simulation results demonstrate the performance improvement over traditional methods, in terms of reachability, delay, stability and frame loss rate.

Block DCT Based Optimization for Wireless SoftCast of Depth Map

With the recent emergence of naked-eye 3D mobile devices, the 3D video-based applications on the mobile terminal are showing great potential. However, the conventional transmission scheme based on separate source coding and channel coding may fail in the broadcast and mobile scenarios due to the drastic changes in wireless channel condition. Recently, an uncoded transmission scheme called SoftCast was introduced, which shows efficient performance for wireless 2D video transmission. However, it may not work well for 3D video wireless transmission, especially for the depth maps. Based on the piecewise smoothness of depth maps, we propose an uncoded wireless depth map transmission scheme, which incorporates three customized components of optimization into the SoftCast framework. First, compared with the frame-level discrete cosine transform (DCT) used in 2D video SoftCast, the mean removed block-based DCT is preferred as the decorrelation transform for depth maps to accommodate its piecewise smooth property, which also facilitates the following subsequent optimization. Second, view synthesis distortion-based chunk dropping is utilized for more efficient power allocation in the case of limited bandwidth, where the block DCT provides a better estimation for synthesis distortion. Third, inter-block DCT coefficients are rearranged to form chunks in terms of minimizing the intra-chunk variance, further enhancing the chunk-level power allocation efficiency. The experimental results demonstrate that the proposed scheme can provide superior performance than the state-of-the-art SoftCast methods while achieving graceful synthesis quality transition within a wide channel signal-to-noise ratio range.

Non-Standard Video Protocol Transmission System Design Based on the Camera Link Interface of FPGA

With the rapid development of video communication technology, video transmission is becoming more and more important. However, in the process of video transmission, the reliability and real-time performance of transmission data is the premise of video high-speed transmission, So In order to improve the real-time video image transmission, validity, reliability, and design a Camera Link interface based on FPGA of non-standard video protocol transmission system, the system front-end by simulating the Camera to complete the video image collection, through the video format conversion chip output standard video protocol data, and uses FPGA to complete the processing of video data, complete the video image with Camera Link interface of high speed transmission, finally realizes the real-time video transmission, but also improve the reliability and effectiveness of the video image.

End-to-End Distortion-Based Multiuser Bandwidth Allocation for Real-Time Video Transmission Over LTE Network

Long Term Evolution (LTE) network is widely used in video transmission because of its high-speed communication capacity. In this paper, an end-to-end distortion-based bandwidth allocation method for multiusers is proposed to enhance video transmission performance of the LTE network. For an LTE network, since throughput is observed as an independent variable which can affect the packet loss ratio, and thus affect the transmission distortion, the end-to-end distortion model is first derived by taking throughput into account. Then, based on the derived end-to-end distortion model, the bandwidth allocation problem is formulated as a convex optimization problem and solved by Karush-Kuhn-Tucker conditions. Simulation results demonstrate the effectiveness of the proposed method. The rate distortion performance of the proposed method is better than the average bandwidth allocation method under different bandwidth utilizations, and is very close to the exhaustive search-based method.

Energy Efficient Power Allocation for Distributed MIMO System

The rapid increasing demands of multi-media applications have busted the improvement of video transmission. By the increase of multimedia transmission, quality of experience (QoE) has become the hot topic of the wireless networks’ research. This paper proposes a QoE-aware resource allocation algorithm in MIMO-OFDM Networks. The power allocation estimation is formulated to maximize according to QoE by considering the video transmission rate and the subcarrier schemes under the constraint of minimum power. In this paper, we will analyses the the performance of video transmission from transmission rate at the application layer to the power allocation at the physical layer. From simulation results, we can see that the proposed subcarrier allocation algorithm maximizes the video QoE by minimizing the sum distortion of multi-users in MIMO networks with power constraints.

Green Video Transmission in the Mobile Cloud Networks

Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods.

CoolConferencing: Enabling Robust Peer-to-Peer Multi-Party Video Conferencing

Multi-party video conferencing (MPVC) is the next big opportunity for Internet streaming. Commercial MPVC solutions are either server-based or peer-to-peer (P2P)-based, which both have performance limitations. P2P technology is expected to dominate the MPVC platform. There are four requirements for a robust MPVC system: 1) realistic network assumptions; 2) realistic system settings; 3) multi-rate support; and 4) any-view support. Existing academic works study the problem from a theoretical perspective, and none of them meets all four requirements simultaneously. We design CoolConferencing, an overlay network for robust P2P MPVC. The core operations follow the easy-to-implement, robust, and resilient data-driven principle, which does not maintain complex global structures such as dissemination trees and can adapt to network dynamic distributedly and quickly. CoolConferencing is a robust system that meets all four requirements simultaneously. In addition, to the best of our knowledge, there is no existing work which examines its MPVC approach under various realistic network environments. We have evaluated CoolConferencing via an event-driven simulation. Compared with state-of-the-art video conferencing solutions, CoolConferencing achieves around 25% gain than Mutualcast and around 9% gain than Celerity in performance. Moreover, when the helper mechanism is enabled, CoolConferencing can easily exploit all available bandwidth to get optimal video transmission performance.

An Adaptive Modulation Technique for Wireless Image Sequence Transmission

Compressed video transmissions can maximize frequency efficiency. However, with regard to image identification, the performance of compressed video transmission is not guaranteed when compared to uncompressed video transmissions. In uncompressed video transmissions, the video-codec can be removed from the devices, which can then solve problems for real-time image transmission and significantly reduce power consumption. In this paper, an effective transmission of uncompressed video is proposed for multi-antennas. When a transmitter sends video data through multiple antennas, an adaptive modulation technique is utilized for enhancing the video quality. The proposed technique adaptively selects a modulation scheme optimized depending on the channel environments. In the proposed scheme, the most significant bit group is transmitted via a selected antenna with good channel environment. From the simulation results, it was found that the peak signal to noise ratio performance of the proposed algorithm improved by about $$3.0\,{\sim }\,4.0\,\hbox {dB}$$3.0~4.0dB in comparison to other conventional algorithms.

Optimal Bandwidth Reservation for Video Streaming in DiffServ Domain

With the emergency of more and more video applications, the problem of providing differentiated Quality of Service (QoS) for various kinds of streams should be solved. DiffServ was proposed to balance transmission guarantee of video and data streams. Existing studies focused on the promotion of video receiving quality by allocating excess resources. In this paper we propose an optimal bandwidth reservation mechanism, aiming at proper amount of bandwidth reservation and good video transmission performance. This mechanism tries to find a moderate reservation according to the video characteristics and buffer size of forwarding routers. Various video sequences and different buffer sizes are employed in the performance evaluation experiments and simulation results verify that (1) Proposed mechanism show its adaptability to data rate, data rate variation and continuous burst of the streaming video. (2) Proposed mechanism presents good performance under various buffer sizes. (3) The amount of bandwidth reservation, which is determined by the streaming video and buffer size of forwarding routers, is moderate to ensure video receiving quality.

Optimization design and experiment of the radio frequency transmission utilized in video capsule endoscope

In view of fuzzy and unstable images received in video capsule endoscopy, and the poor communication effect, an improved radio frequency (RF) transmission circuit module with low power consumption and helix antenna is designed to improve the performance of video transmission and to extend the working time of the capsule. Simulation and analysis of the circuits and antenna with HFSS12 and ADS2011 software are highlighted. Video capsule endoscope prototypes are made and evaluated to verify the feasibility of the proposed design. In vitro tests and animal experiments show that the results of the antenna tests basically meet the simulation, and the power consumption of RF circuit is 15.9mV. The video capsule endoscope works well with the receiver with multiple receiving antennas, and the working time is more than 9 h. It can realize the wireless transmission of the video data, and images received are clear and stable in comparison with the previous designs.

Scalable Video Transmission Over Wireless Networks Based on Loss Distribution and Layer Information

We propose an efficient scalable video transmission method using unequal forward error correction (FEC) scheme based on loss distribution and layer information. The actual packet loss rates over wireless networks are variable due to the clustered packet losses. First, we deduce the expected packet loss recovery rate from the estimation of the effective packet loss rate using loss distribution statistics. Second, layer-based distortion metric is presented including error propagation effects from hierarchical prediction structure in compressed scalable video packets. Third, the performance metric for FEC assignment is proposed from the layer-based distortion metric and the expected packet loss recovery rate. Finally, the FEC assignment algorithm is proposed for maximizing the performance metric. The proposed unequal FEC assignment method demonstrates robustness and significantly improved performance for scalable video transmission in various channel conditions compared to previous FEC assignment schemes.

Optimal Foresighted Multi-User Wireless Video

Recent years have seen an explosion in wireless video communication systems. Optimization in such systems is crucial – but most existing methods intended to optimize the performance of multi-user wireless video transmission are inefficient. Some works (e.g., Network Utility Maximization (NUM)) are myopic: they choose actions to maximize instantaneous video quality while ignoring the future impact of these actions. Such myopic solutions are known to be inferior to foresighted solutions that optimize the long-term video quality. Alternatively, foresighted solutions such as rate-distortion optimized packet scheduling focus on single-user wireless video transmission, while ignoring the resource allocation among the users. In this paper, we propose a general framework of foresighted resource allocation among multiple video users sharing a wireless network. Our framework allows each user to flexibly choose individual cross-layer strategies. Our proposed resource allocation is optimal in terms of the total payoff (e.g., video quality) of the users. A key challenge in developing foresighted solutions for multiple video users is that the users' decisions are coupled. To decouple the users' decisions, we adopt a novel dual decomposition approach, which differs from the conventional optimization solutions such as NUM, and determines foresighted policies. Specifically, we propose an informationally-decentralized algorithm in which the network manager updates state- and user-dependent resource “prices” (i.e., the dual variables associated with the resource constraints), and the users make individual packet scheduling decisions based on these prices. Because a priori knowledge of the system dynamics is almost never available at run-time, the proposed solution can learn online while performing the foresighted optimization. Simulation results show 7 dB and 3 dB improvements in Peak Signal-to-Noise Ratio (PSNR) over myopic solutions and existing foresighted solutions, respectively.

Cross Layer Design for Video Streaming in MANETs

Mobile Ad hoc NETworks (MANETs) are becoming more essential to wireless communications due to growing popularity of mobile devices. However, MANETs do not seem to effectively support multimedia applications and especially video transmission. In this work, we propose a cross-layer design that aims to improve the performance of video transmission using TCP Friendly Rate Control (TFRC). Our design provides priority to video packets and exploits information from the MAC layer in order to improve TFRC's performance. The proposed cross-layer design utilizes SNR measurements along the routing path, in order to make the route reconstruction procedure more efficient. Simulation results show that both the use of traffic classification and the SNR utilization lead to important improvements in terms of end-to-end Quality of Service (QoS).

Rate Allocation Algorithm with Successive Refinement in Peer-to-Peer Networks

We introduce a new across-peer rate allocation algorithm with successive refinement to improve the video transmission performance in P2P networks, based on the combination of multiple description coding and network coding. Successive refinement is implemented through layered multiple description codes. The algorithm is developed to maximize the expected video quality at the receivers by partitioning video bitstream into different descriptions depending on different bandwidth conditions of each peer. Adaptive rate partition adjustment is applied to ensure the real reflection of the packet drop rate in the network. Also the granularity is changed to the scale of atomic blocks instead of stream rates in prior works. Through simulation results we show that the algorithm outperforms prior algorithms in terms of video playback quality at the peer ends, and helps the system more adjustable to the peer dynamics.