Joint resource allocation for MIMO-OFDM video transmission systems with unequal error protection

To improve the performance of MIMO-OFDM video transmission systems on the limitation of wireless bandwidth and transmitting power,we propose an adaptive joint resource allocation algorithm with unequal error protection(UEP) based on joint source-channel coding(JSCC) according to H.264 video compression standard and RCPT channel coding.According to different thresholds of the average SNR of subchannels,the algorithm dynamically allocates the source coding parameters of original video data and the channel coding parameters of RCPT,which realizes UEP for the compressed video data of different importance.Through the bit and power allocation based on MQAM modulation and the subspace allocation based on beamforming technology for different subcarriers,an adaptive joint resource allocation making full use of space-frequency domain resources have been realized.The simulation results indicate that the algorithm improves the adaptability of video transmission systems in different wireless environments and the quality of video retrieval.

A NOVEL CROSS-LAYER STRATEGY OF VIDEO TRANSMISSION TO SOLVE PERFORMANCE ANOMALY IN MULTI-RATE IEEE 802.11 NETWORKS

When wireless hosts use different rates to transmit data in IEEE 802.11 networks, it will take on the state of performance anomaly which will severely decrease the throughputs of all the higher rate hosts. Hence, it is bad for video service transmission. Considering that video is very sensitive to packet delivery delay but can tolerate some packet losses, we propose a novel cross-layer scheme which takes these two characteristics into consideration. Firstly, the maximum number of retransmissions for a video Medium Access Control (MAC) frame is computed in MAC layer according to video frame rate requirement of application layer and current access delay of MAC layer. Secondly, within the margin of the tolerant Packet Loss Rate (PLR) of application layer, several video MAC frames are allowed to drop so that we can adaptively select the transmission rate as high as possible for the rest of video MAC frames in terms of current channel quality and the maximum number of retransmissions. Experiment results show that the proposed method can reduce the delay and jitter of video service and improve the throughputs of fast hosts. Therefore, it increases the quality of reconstructed video to a certain extent and relieves the performance anomaly of network effectively.

Implementation of IP Multicast in LAN Video Transmission Based on Winsock2

With the development of IP multicast technology, the best use of network bandwidth can be achieved, and IP video broadcasting can be implemented. This paper introduces the principle of IP multicast technology, and presents an implementation of IP video multicasting in LAN based on Winsock2. Problems in IP multicast are also analyzed in brief.

Joint Optimization Strategy for Video Transmission over Distributed Cognitive Radio Networks

A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in nature.However,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all clients.The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences.Besides,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization algorithms.With this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart cities.The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive.Besides,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects.The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.

SST-V:A Scalable Semantic Transmission Framework for Video

The emerging new services in the sixth generation(6G)communication system impose increasingly stringent requirements and challenges on video transmission.Semantic communications are envisioned as a promising solution to these challenges.This paper provides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm,named scalable semantic transmission framework for video(SST-V),which jointly considers the semantic importance and channel conditions.Specifically,a semantic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level,facilitating high-efficiency semantic coding.By further considering the channel condition,a cascaded learning based scalable joint semanticchannel coding algorithm is proposed,which autonomously adapts the semantic coding and channel coding strategies to the specific signalto-noise ratio(SNR).Simulation results show that SST-V achieves better video reconstruction performance,while significantly reducing the transmission overhead.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Robust spatio-temporal error concealment for packet- lossy H.264 video transmission

In this article, a spatio-temporal post-processing error concealment algorithm designed initially for a H.264 video-streaming scheme over packet-lossy networks has been presented. It aims at optimizing subjective quality of restored video and the conventional objective metric, peak signal-to-noise ratio （PSNR）, as well, under the constraints of low delay and computational complexity, which are critical to real-time applications and portable devices having limited resources. Specifically, it takes into consideration physical property of motion to achieve more meaningful perceptual video quality. Further, a content-adaptive bilinear spatial interpolation approach and a temporal error concealment approach are combined under a unified boundary match criterion based on texture and motion activity analysis. Extensive experiments have demonstrated that the proposal not only result in better reconstruction, objectively and subjectively, than the reference software model benchmark, but also results in better robustness to different video sequences.

Adaptive efficient video transmission over the Internet based on congestion control and RS coding

An approach based on adaptive congestion control and adaptive error recovery with RS (Reed-Solomon) coding method is presented for efficient video transmission over the Internet. Featured by weighted moving average rate control and TCP-friendliness, AVSP, a novel adaptive video streaming protocol, is designed with adjustable rate control parameters so as to respond quickly to the QoS status fluctuation during video transmission over the Internet. Combined with congestion control policy, an adaptive RS coding error recovery scheme with variable parameters is presented to enhance the robustness of MPEG video transmission over the Internet with restriction to the total system bandwidth .

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.

Reducing Video Transmission Cost of the Cloud Service Provider with QoS-Guaranteed

With the advancement of cloud computing technology,many service providers are combining with cloud service providers to build a highly available streaming video-on-demand cloud platform and provide video services to end users.Generally,cloud service providers deploy many edge cloud CDN nodes in different geographic areas and provide video services to end users.However,when an end-user wants to watch certain videos and request video resources from surrounding edge cloud CDN nodes,the edge cloud CDN node will request missing video clips from other cloud nodes.Therefore,this will generate a large amount of additional video transmission costs and reduce the quality of service of the cloud service provider.To reduce or even minimize the video transmission cost of edge cloud CDN nodes while ensuring the quality of service(QoS).We designed a video transmission algorithm called Netdmc to ensure transmission quality.The algorithm can be divided into two parts.The first part is a low-latency video request algorithm based on ensuring service quality,and the second part is a video request algorithm based on minimizing video transmission costs.The simulation results demonstrate that the Netdmc algorithm can effectively reduce the cost of cloud service providers and ensure the quality of video services.

Multiple description video coding using GOB alternation and low quality macroblock update

To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame is first created by group of blocks （GOB） alternation. Motion information is then duplicated in both the descriptions and a process called low quality macroblock update is designed to redundantly encode textures in each frame using standard bit stream syntax. In this way, the output bit streams are standard compliant and better trade-offs between redundancy and single charmel reconstruction distortion are achieved. The proposed method has much better performance than the well-known MD transform coding （MDTC） method both in terms of redundancy rate distortion, and in the packet loss scenario.

Efficient Multiple Description Scalable Video Coding Scheme Based on Weighted Signal Combinations

A simple, efficient multiple description coding （MDC） algorithm was developed based on weighted signal combinations. The scheme uses the standard video encoder with a pre-processing stage to generate multiple descriptions. The decoder then uses a post-processing algorithm to combine the descriptions to provide better image quality. A scalable codec in the MDC system allows the system to provide multiple descriptions and scalability at the same time. In addition, since the different scalable descriptions may have different qualities, a simple averaging process is not optimal. An optimal weighted combination of the two descriptions was developed based on the signal to noise ratios. Compared with the simple average combination, the algorithm significantly improved the video quality, especially with large quality differences between the two descriptions, with gains of up to 3.56 dB.

Underwater video transceiver designs based on channel state information and video content

Underwater hostile channel conditions challenge video transmission designs. The current designs often treat video coding and transmission schemes as individual modules. In this study, we develop an adaptive transceiver with channel state information(CSI) by taking into account the importance of video components and channel conditions. The design is more effective than the traditional ones. However, in practical systems, perfect CSI may not be available. Therefore, we compare the imperfect CSI case with existing schemes, and validate the effectiveness of our design through simulations and measured channels in terms of a better peak signal-to-noise ratio and a higher video structural similarity index.

Free-space communication based on quantum cascade laser

A free-space communication based on a mid-infrared quantum cascade laser （QCL） is presented. A room-temperature continuous-wave distributed-feedback （DFB） QCL combined with a mid-infrared detector com- prise the basic unit of the communication system. Sinusoidal signals at a highest frequency of 40 MHz and modu- lated video signals with a carrier frequency of 30 MHz were successfully transmitted with this experimental setup. Our research has provided a proof-of-concept demonstration of space optical communication application with QCL. The highest operation frequency of our setup was determined by the circuit-limited modulation bandwidth. A high performance communication system can be obtained with improved modulation circuit system.

Error detection based on MB types

This paper proposes a method of error detection based on macroblock （MB） types for video transmission. For decoded inter MBs, the absolute values of received residues are accumulated. At the same time, the intra textural complexity of the current MB is estimated by that of the motion compensated reference block. We compare the inter residue with the intra textural complexity. If the inter residue is larger than the intra textural complexity by a predefined threshold, the MB is considered to be erroneous and errors are concealed. For decoded intra MBs, the connective smoothness of the current MB with neighboring MBs is tested to find erroneous MBs. Simulation results show that the new method can remove those seriously-corrupted MBs efficiently. Combined with error concealment, the new method improves the recovered quality at the decoder by about 0.5--1 dB.