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For the joint impact of carrier frequency offset (CFO) and phase noise (PHN), the exact closed-form signal-to-interference-noise ratio (SINR) expressions are derived for novel Walsh Hadamard (WH)-spread filterbank multicarrier (SFBMC) transmission scheme in integrated satellite–terrestrial broadcast system (ISTBS). For this evaluation, variable bit rates (VBR)s for WH-SFBMC are considered for transmission in 3-state Fontan land mobile satellite suburban environment. Further, symbol error rate (SER) and spectral efficiency are analyzed based on the obtained SINR expressions. Such analyses are conducted to support VBR quality of service (QoS) in orthogonal frequency division multiplexing (OFDM)-based digital video broadcasting standards utilizing ISTBS. Furthermore, pilot-based channel estimation error variance is evaluated for VBR WH-SFBMC. Later, block-by-block system computational complexity of VBR WH-SFBMC, perfect reconstruction (PR)-filterbank multicarrier (FBMC), and OFDM schemes are evaluated. It is shown that, VBR WH-SFBMC is rate-dependent and relatively more complex system than single rate PR-FBMC and OFDM. However, the SER performance of WH-SFBMC is illustrated to outperform PR-FBMC and OFDM significantly even for higher bit rates in presence of CFO and PHN. Moreover, simulation and analytic results are in close approximation with each other. Thus, a trade-off exists between achieved error performance and system complexity.

Traditional video compression approaches build upon the hybrid coding framework with motion-compensated prediction and residual transform coding. In this paper, we propose the first end-to-end deep video compression framework to take advantage of both the classical compression architecture and the powerful non-linear representation ability of neural networks. Our framework employs pixel-wise motion information, which is learned from an optical flow network and further compressed by an auto-encoder network to save bits. The other compression components are also implemented by the well-designed networks for high efficiency. All the modules are jointly optimized by using the rate-distortion trade-off and can collaborate with each other. More importantly, the proposed deep video compression framework is very flexible and can be easily extended by using lightweight or advanced networks for higher speed or better efficiency. We also propose to introduce the adaptive quantization layer to reduce the number of parameters for variable bitrate coding. Comprehensive experimental results demonstrate the effectiveness of the proposed framework on the benchmark datasets.

This work presents a comparison study using a constant bitrate (CBR) and variable bitrate (VBR) configuration on lossy audio formats, which can be chosen as suitable option to compress spacecraft onboard acoustic data. The acoustic data measured during a rocket motor firing test is assessed by comparing the signal compression rates and information distortions yielded by using those configurations. As a result, the compression algorithm to be used for launch vehicle onboard acoustic measurements is defined. The results have shown that among the lossy formats presented, the OPUS format using the VBR configuration has achieved a compression rate of 254:1 at 5 kbps, saving up to 99.6% the required amount of data to be transmitted through a telemetry link when compared to a non compressed data.

A new type of synchronous machine modeling is presented in this paper. Initially, a general theory of machine modeling was derived, and then extended to the case of machine modeling in the form of the so-called. VBR winding. The advantages and disadvantages of this approach in relation to the traditional QD representation of synchronous machines are explained, and then the model is extended to include magnetic saturation of the machine. After deriving the equations in detail, the machine model was written in programming language ​​and implemented in the "Typhoon HIL control center" software library. The model was tested in detail in the conditions of steady state, dynamic processes and saturation areas using analytical formulas and software environment "ANSYS Maxwell".

Sparse code multiple access (SCMA) is one of the competitive non-orthogonal multiple access (NOMA) techniques for the next generation communication systems. In this paper, we put forward a simple and efficient design method named divided extended mother codebook (DEMC) to construct SCMA codebooks based on golden angle modulation (GAM) constellation points. First, we generate a vector defined by the GAM. Second, we design the extended mother codebook ( $\mathbf {EMC}$ ) by introducing the power and phase dependent constraints of symbols in multi-dimension codewords. The power constraints can ensure the power of each codeword is the same which leads to the optimal peak to average power ratio (PAPR) especially for the uplink channel. Third, we divide the $\mathbf {EMC}$ into several mother codebooks ( $\mathbf {MC}\text{s}$ ) to generate the constant bit rate (CBR) or variable bit rate (VBR) SCMA codebooks. The structure of the VBR SCMA codebooks is compatible with that of CBR. The VBR codebooks and CBR codebooks use the same factor graph, therefore, the users employed the VBR codebooks can also utilize the efficient message passing algorithm (MPA) for multi-user detection. Simulations reveal that the bit error rate (BER) performance of the proposed CBR DEMC-SCMA codebooks is outstanding with low complexity. The VBR DEMC-SCMA codebooks can flexibly satisfy the different service requirements, and the BER performance of the VBR DEMC-SCMA codebooks is close to each other though their modulation orders are various. This feature shows that the users can apply for a high order codebook to get a faster information transfer rate without increasing transmission power and bandwidth.

Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis.

Due to the increase in video streaming traffic over the Internet, more innovative methods are in demand for improving both Quality of Experience (QoE) of users and Quality of Service (QoS) of providers. In recent years, HTTP Adaptive Streaming (HAS) has received significant attention from both industry and academia based on its impacts on the enhancement of media streaming services. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the Network Operators’ infrastructure and evolving network conditions. Along with the development of future Internet infrastructure, Software-Defined Networking (SDN) has been researched and newly implemented as a promising solution in improving services of different Internet layers. In this paper, we present a novel architecture incorporating bitrate adaptation and dynamic route allocation. At the client side, adaptation logic of VBR videos streaming is built based on the MPEG-DASH standard. On the network side, an SDN controller is implemented with several routing strategies on top of the OpenFlow protocol. Our experimental results show that the proposed solution enhances at least 38% up to 185% in term of average bitrate in comparison with some existing solutions as well as achieves better viewing experience than the traditional Internet.

Broadcasting has been proved to be an efficacious approach of video-on-demand (VOD) service. Over the past few decades, many scholars have devoted a great deal of time to the study of broadcasting. Among all available schemes, harmonic broadcasting (HB) scheme points out the minimum bandwidth required to broadcast a popular video given a fixed viewers’ waiting time. HB divides each segment equally into several horizontal subsegments in line with the harmonic series. However, such segmentation is too complex to be feasible. To solve this problem, we come up with an original scheme dubbed wrap HB (WHB), in which we partition each segment equally into vertical subsegments according to its serial number. Given a fixed channel allocation, the maximum number of segments of our scheme approaches to the theoretical upper bound. Compared to the fast broadcasting scheme and the recursive frequency-splitting scheme, the buffer requirement in the WHB scheme decreases by 26%. We also design a simple VBR WHB (SVWHB) scheme to accommodate VBR video based on the WHB scheme. The comprehensive simulation results indicate that the SVWHB scheme has similar savings as the WHB scheme.

We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream. In contrast to existing learned variable bitrate solutions which produce separate bitstreams for each quality, it enables easier rate-control and requires less storage. Leveraging the latent scaling based variable bitrate solution, we introduce nested quantization, a method that defines multiple quantization levels with nested quantization grids, and progressively refines all latents from the coarsest to the finest quantization level. To achieve finer progressiveness in between any two quantization levels, latent elements are incrementally refined with an importance ordering defined in the rate-distortion sense. To the best of our knowledge, PLONQ is the first learning-based progressive image coding scheme and it outperforms SPIHT, a well-known wavelet-based progressive image codec.

A fuzzy rate controller with buffer constraint in combination with a perceptual quality controller is proposed for streaming applications of the AVC/H.264 scalable (SVC) video. The bit rate of each video layer is controlled separately by the fuzzy controller that adjusts the quantization parameter (QP) on a group of pictures (GOP) basis. The QPs of pictures are computed from the GOP QP by the well-known QP cascading technique. While the fuzzy controller provides the buffering constraint for each video layer, the quality controller tries to improve the perceptual quality of the compressed video based on the foveated just-noticeable distortion (FJND) model. The quality controller regulates the QP of each macroblock around the picture QP based on the visibility threshold of the FJND model. In these applications, the initial buffering allows slight variations of the bit rate leading to produce a variable bit rate (VBR) video bit stream with consistent quality. Experimental results show that the proposed algorithm effectively adapts to the buffer size, while strictly prevents buffer overflow and underflow. In addition, incorporating the perceptual quality controller into the fuzzy rate controller achieves higher perceptual quality at the same bit rate.

Six-phase electrical machines are often found in special purpose applications, such as vehicular, ship, and aircraft power systems, and are now becoming considered in renewable energy generation. For design and analysis of such power systems, accurate and numerically efficient models are required for various transient simulation programs. Recently, a constant-parameter voltage-behind-reactance (CPVBR) model has been developed for magnetically linear six-phase synchronous machines as an alternative to the conventional qd 0 and VBR machine models. In this paper, a saturable CPVBR model is presented for six-phase machines, which includes the main flux saturation and achieves magnetically decoupled and constant RL interfacing branches. The new magnetically decoupled CPVBR (DCPVBR) model has many advantages for implementation in commonly available simulation programs where it can be easily interfaced with inductive and/or power-electronic circuit elements. The proposed DCPVBR model is demonstrated to have improved computational performance compared to the conventional qd 0 and VBR models.

VoIP traffic classification plays a major role towards network policy enforcements. Characterization of VoIP media traffic is based on codec behaviour. With the introduction of variable bit rate codecs, coding, compression and encryption present different complexities with respect to the classification of VoIP traffic. The randomness tests do not extend directly to classification of compressed and encrypted VoIP traffic. The paper examines the applicability of randomness tests to encrypted and unencrypted VoIP traffic with constant bit rate and variable bit rate codecs. A novel method Construction-by-Selection that constructs a test sequence from partial payload data of VoIP media session is proposed in this paper. The results based on experimentations on this method show that such construction exhibit randomness and hence allows differentiation of encrypted VoIP media traffic from unencrypted VoIP media traffic even in the case of variable bit rate codecs.

Random linear network coding (RLNC) can enhance the reliability of multimedia transmissions over lossy communication channels. However, RLNC has been designed for equal size packets, while many efficient multimedia compression schemes, such as variable bitrate (VBR) video compression, produce unequal packet sizes. Padding the unequal packet sizes with zeros to the maximum packet size creates an overhead on the order of 20%–50% or more for typical VBR videos. Previous padding overhead reduction approaches have focused on packing the unequal packet sizes into fixed size packets, e.g., through packet bundling or chaining and fragmentation. We introduce an alternative padding reduction approach based on coding macro-symbols (MSs), whereby an MS is a fixed-sized part of a packet. In particular, we introduce a new class of RLNC, namely MS RLNC which conducts RLNC across columns of MSs, instead of the conventional RLNC across columns of complete packets of equal size. Judiciously arranging the source packets into columns of MSs, e.g., through shifting the source packets horizontally relative to each other, supports favorable MS RLNC coding properties. We specify the MS RLNC encoding and decoding mechanisms and analyze their complexity for a range of specific MS arrangement strategies within the class of MS RLNC. We conduct a comprehensive padding overhead evaluation encompassing both previous approaches of packing the unequal size packets into fixed size packets as well as the novel MS RLNC approaches with long VBR video frame size traces. We find that for small RLNC generation sizes that support low network transport delays, MS RLNC achieves the lowest padding overheads; while for large generation sizes, both the previous packing approaches and the novel MS RLNC approaches effectively reduce the padding overhead.

Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic adaptive streaming over HTTP (DASH) becomes the de facto standard of video streaming and it is adopted by Youtube, Netflix, and so on. Despite of the popularity, network traffic during video streaming shows an identifiable pattern which brings threat to user privacy. In this paper, we propose a video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bitrate trend remains relatively stable because of the widely used variable bit-rate (VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR-based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, we propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. We evaluate our attack method in different scenarios for various video content, segment lengths, and quality levels. The experimental results show that the identification accuracy can reach up to 90% using only three-minute continuous network traffic eavesdropping.

To fulfill the increasing demand for high speed Variable Bit Rate (VBR) broadcast services with reliable Quality of Service, Group Orthogonal-Orthogonal Frequency Division Multiple Access (GO-OFDMA) and Variable Spreading Length Multicarrier Code Division Multiple Access transmission schemes are examined over Land Mobile Satellite (LMS) channel. This paper presents the performance analysis of aforementioned multiple access transmission schemes in terms of computation complexity, Peak-to-Average-Power-Ratio (PAPR) and the average Bit Error Rate (BER) performance over L-band LMS channel. Minimum Mean Square Equalizer (MMSE) equalizer is employed at the receiver. Based on the simulation results, GO-OFDMA stems out to be a better variable rate transmission scheme in respect of both PAPR and BER performance for higher data rate users. Further, GO-OFDMA based VBR transmitter and MMSE equalizer are prototyped on commercially available virtex5 xc5vlx110t–1ff1136 Field Programmable Gate Array (FPGA) device. A block-by-block analysis of hardware resource utilization and power dissipation for the VBR GO-OFDMA transmitter is provided. The achieved baud rate for VBR GO-OFDMA transmission scheme on FPGA is 103.874 Msps. Thus, GO-OFDMA transmission scheme could be appropriate for VBR communication over LMS channel.

Dynamic Adaptive Streaming over HTTP(DASH) is a video transmission ptotocol to adapt to different network conditions and heterogeneous client devices. The client first requests and parses the Media Presentation Description(MPD) file from the DASH server to obtain basic information including the average bitrate list and segment URLs. After that, the adaptive bitrate(ABR) algorithm decides the quality of the next requested segment based on the network and buffer conditions. However, there is a big discrepancy between the segment size calculated with this coarse-grained average bitrate and the real size due to the constantly changing video scenes, which is especially obvious for variable bitrate(VBR) encoded videos. And this error is transparent to the ABR algorithm. This paper first analyzes and confirms the inter-layer correlation of scalable video coding(SVC) encoded contents, then designs a segment size prediction module to cooperate with the ABR algorithm. Experimental results show that predicting a certain enhancement layer(EL) segment size by all its previous layers can increase the probability that the predicted value falls within the accurate interval by 20% −58% compared to predicting the EL only by base layer(BL). Besides, the ABR algorithm assisted with size prediction module can increase the average bitrate by 29.2%, reduce the average bitrate switches by 13.4% and the average rebuffering events by 78.7% compared with the independent ABR algorithm.

Safe lock-free memory reclamation is a difficult problem. Existing solutions follow three basic methods (or their combinations): epoch based reclamation, hazard pointers, and optimistic reclamation. Epoch-based methods are fast, but do not guarantee lock-freedom. Hazard pointer solutions are lock-free but typically do not provide high performance. Optimistic methods are lock-free and fast, but previous optimistic methods did not go all the way. While reads were executed optimistically, writes were protected by hazard pointers. In this work we present a new reclamation scheme called version based reclamation (VBR), which provides a full optimistic solution to lock-free memory reclamation, obtaining lock-freedom and high efficiency. Speculative execution is known as a fundamental tool for improving performance in various areas of computer science, and indeed evaluation with a lock-free linked-list, hash-table and skip-list shows that VBR outperforms state-of-the-art existing solutions., LIPIcs, Vol. 209, 35th International Symposium on Distributed Computing (DISC 2021), pages 35:1-35:18

The ongoing COVID-19 pandemic has pointed out, even more, the important need for hygiene contactless biometric recognition systems. Vein-based devices are great non-contact options although they have not been entirely well-integrated in daily life. In this work, in an attempt to contribute to the research and development of these devices, a contactless wrist vein recognition system with a real-life application is revealed. A Transfer Learning (TL) method, based on different Deep Convolutional Neural Networks architectures, for Vascular Biometric Recognition (VBR), has been designed and tested, for the first time in a research approach, on a smartphone. TL is a Deep Learning (DL) technique that could be divided into networks as feature extractor, i.e., using a pre-trained (different large-scale dataset) Convolutional Neural Network (CNN) to obtain unique features that then, are classified with a traditional Machine Learning algorithm, and fine-tuning, i.e., training a CNN that has been initialized with weights of a pre-trained (different large-scale dataset) CNN. In this study, a feature extractor base method has been employed. Several architecture networks have been tested on different wrist vein datasets: UC3M-CV1, UC3M-CV2, and PUT. The DL model has been integrated on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 smartphones obtaining high biometric performance, up to 98% of accuracy and less than 0.4% of EER with a 50–50% train-test on UC3M-CV2, and fast identification/verification time, less than 300 milliseconds. The results infer, high DL performance and integration reachable in VBR without direct user-device contact, for real-life applications nowadays.

The demand for Wireless Fidelity (WiFi) is increasing tremendously. Due to a lack of cellular signal strength, ongoing cellular calls may suffer a degradation in Quality-of-Service (QoS) or become disconnected from the cell tower. Voice over WiFi (VoWiFi) is the best alternative in this scenario, because it provides users with the same benefits as they receive from the cellular tower. In our study, we have considered the VoWiFi as Voice over Internet Protocol (VoIP) over WiFi. To prevent the bandwidth wastage due to background noise, we have incorporated silence suppression for Variable Bit Rate (VBR) traffic. As a result, the capacity of VoWiFi cell can be increased relative to the Constant Bit Rate (CBR) traffic. Additionally, we used Arbitration Inter-frame Spacing (AIFS) and Compressed RTP (cRTP) to increase the VoWiFi cell capacity of IEEE 802.11ac (i.e. fifth generation Wireless Local Area Network (WLAN) standard) Access Point (AP). We used the DCF Interframe Spacing (DIFS) and AIFS to compare the results of CBR and VBR traffics. Furthermore, we applied the retransmission mechanism to avoid VoWiFi voice packet loss and compared the results using DIFS and AIFS to determine their effectiveness.

Nowadays, Internet has become a crucial part of everyday life in areas of academic research, healthcare, military, commercial industries, transportation, and entertainment. The Internetwork was not invented for its present-day state of operation. Numerous limitations are currently emerging in terms of Internet performance, mobility, scalability, dependability, security, and service quality. In this paper we consider real time network application for voice and video, both having variable bitrate sources. A mobile producer is solely responsible to direct the traffic to a specified destination, either in one network or different. For voice traffic, a constant bitrate (CBR) is generated in real time during a talkspurt. During this generation interval, a small amount of traffic is transmitted in a period of silent pause. For video traffic, the bitrate is changed because video coding form is altering due to time interval. Also, the visible image in the proposed video application varies due to network coding application. In the results, performance analysis is measured due to amount of time spend in each cell residence and signalling cost between mobile producer and rendezvous node as a definite destination. We conducted simulation coding in ndnSIM 2.1 and performance analysis using Python. Simulation output present better results compared with other benchmarked research.

In the typical transcoding pipeline for adaptive video streaming, raw videos are pre-chunked and pre-encoded according to a set of resolution-bitrate or resolution-quality pairs on the server-side, where the pair is often named as bitrate ladder. Different from existing heuristics, we argue that a good bitrate ladder should be optimized by considering video content features, network capacity, and storage costs on the cloud. We propose DeepLadder, a per-chunk optimization scheme which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder w.r.t the above concerns. Technically, DeepLadder selects the proper setting for each video resolution autoregressively. We use over 8,000 video chunks, measure over 1,000,000 perceptual video qualities, collect real-world network traces for more than 50 hours, and invent faithful virtual environments to help train DeepLadder efficiently. Across a series of comprehensive experiments on both Constant Bitrate (CBR) and Variable Bitrate (VBR)-encoded videos, we demonstrate significant improvements in average video quality bandwidth utilization, and storage overhead in comparison to prior work as well as the ability to be deployed in the real-world transcoding framework.

Variable Bitrate (VBR) video encoding can provide much high quality-to-bits ratio compared to the widely adopted Constant Bitrate (CBR) encoding, and thus receives significant attentions by content providers in recent years. However, it is challenging to design efficient adaptive bitrate algorithms for VBR-encoded videos due to the sharply fluctuating chunk size and the resulting bitrate burstiness. In this paper, we propose a neural adaptive streaming framework called Vibra for VBR-encoded videos, which can well accommodate the high fluctuation of video chunk sizes and improve the quality-of-experience (QoE) of end users significantly. Our framework takes the characteristics of VBR-encoded videos into account, and adopts the technique of deep reinforcement learning to train a model for bitrate adaptation. We also conduct extensive trace-driven experiments, and the results show that Vibra outperforms the state-of-the-art ABR algorithms with an improvement of 8.17% -- 29.21% in terms of the average QoE.

The popularity of Wireless Local Area Network (WLAN) is increasing tremendously. Recent research indicates that the number of voice calls accommodated in the IEEE 802.11 a/b/g/n standards supported Access Point (AP) is limited. To enhance the capacity of Voice over WiFi (VoWiFi), we have used variable bit rate (VBR) traffic instead of constant bit rate (CBR) traffic. We have seen that the Voice over Internet Protocol (VoIP) traffic is half-duplex due to a single talk conversation. To avoid the wastage of bandwidth resources, we have used silence suppression, which prevents background noise and generates VBR traffic. In this paper, we have proposed an analytical model to estimate the approximate number of VoWiFi calls possible in IEEE 802.11ac standard AP using VBR traffic. Further, we have used Compressed RTP (cRTP) and Arbitration Inter-frame Spacing (AIFS) in our derived model to improve the VoWiFi cell capacity.

In wireless communication, speech is an essential service for which low rate speech coding is a key technique. Reducing the transmission bandwith and achieving higher speech quality are primary concerns in developing new speech coding algorithms. This report develops a joint channel and source controlled adaptive variable bit-rate algorithm to achieve a high speech quality and maintain an efficient spectrum.

Managing Quality of Experience (QoE) for users streaming videos over cellular networks (e.g., 4G LTE net-works) is an important problem. In this paper, we consider the problem of streaming multiple variable bit rate (VBR) video streams to users over heterogeneous cellular networks (HetNets) comprising of a cellular infrastructure and storage-enabled femtocells that are capable of caching content. We assume that each user can be connected to multiple femtocells simultaneously with the wireless channel quality between users and femtocells varying spatially as well as temporally. We address the joint assignment and scheduling problem, where the assignment problem is to match users to femtocells from where the video can be downloaded and the scheduling problem is to fairly allocate time slots to the different users that are assigned to the same femtocell. Our objective is to maximize the total bit rate received by the users considering all femtocells, subject to the QoE-based video streaming constraint that the number of stalls received by the users is bounded. We formulate the joint assignment and scheduling problem as an optimization problem and develop QoE-aware greedy algorithms that tackle the assignment and scheduling problems separately. We conduct trace-based experiments using real-world 4G LTE wireless connectivity and VBR video traces and demonstrate that our solution outperforms baselines in terms of average number of stalls per user (48% less on average) and results in a fair distribution of stalls across all users.

Teleoperation of an unmanned aerial vehicle (UAV) is a challenging mobile application with real-time control from a first-person view. It poses stringent latency requirements for both video and control traffic. This paper studies the video quality and latencies for UAV teleoperation over LTE using ns3 simulations. A key ingredient is the latency budget model. We observe that the latency of the video is higher and more sensitive to mobility than that of the control traffic. The latency is influenced by the traffic variation caused by the variable bit rate of the streaming application. High mobility tends to increase latency and lead to more outliers, being problematic in real-time control.

The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out for the factors impacting QoE, and their behavior is evaluated. The statistical data is taken from real architecture and analysis. Likewise, we have compared the response time and throughput with the varying segment size in adaptive bitrate video streaming. Moreover, resource usage is also analyzed by incorporating the effect of CPU consumption and energy consumption over segment size, which will be counted as effective efforts for sustainable development of multimedia systems. The proposed architecture is validated and indulged as a core component for video streaming based on the use case of a Mobile IPTV solution for 4G/LTE Users.

In the past few years, video streaming over cellular networks has become increasingly popular due to which managing end-user Quality of Experience (QoE) has become a crucial problem. We consider the problem of variable bit rate (VBR) video streaming over heterogeneous cellular networks (HetNets). We address this problem as a joint assignment and scheduling problem and design QoE-aware greedy algorithms to solve it. The greedy assignment algorithm links users to femtocells for downloading the videos from, and the greedy scheduling algorithm fairly allocates time slots to the users assigned to a specific femtocell. Our experiments on real-world 4G LTE wireless connectivity and VBR video traces show the superior performance of our algorithm.

Video content over the Internet is growing rapidly. There arises a need for more powerful and proficient video compression techniques to handle beating stress of voluminous video over the limited bandwidth. Traditional video compression mechanisms are hand-designed, and their architecture is an amalgamation of different modules designed in such a way that different modules are optimized individually instead of achieving end-to-end optimization of the whole network. The positive upshots of deep learning in image compression emerged a breakthrough for video compression as well. ConvGRU, a convolutional recurrent neural network, comprises productive edges of both RNN and CNN. The proposed architecture consists of ConvGRU as basic building blocks implemented in both fixed and variable bit rate models. The experimental results demonstrated that randomized emission step ConvGRU-based architecture gives better performance and provides a base framework for further optimization enhancements.

29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK, Having an influence on the effective utilization of network resources, optimum video streaming in various network conditions have a significant role. Therefore, optimization studies on DASH receives considereable attention from the researchers. Although most of these studies focus on developing adaptation algorithm in the client side, the effect of decisions made during video encoding on the perceived quality cannot be underestimated. In this study, the effect of encoding mode selection on the perceived quality for scalable video coding is investigated. Streaming of videos which are encoded in CBR and VBR modes are tested with extensive network conditions by implementing three different adaptation algorithm. It is observed that VBR encoding has superior performance with a considerable margin in terms of both video stall duration and video quality switching frequency., IEEE,IEEE Turkey Sect

Bitrate adaptation algorithms have received considerable attention recently. In order to evaluate these algorithms objectively, multiple DASH datasets have been proposed. However, only few of them are compatible to SVC-based adaptation algorithms. Apart from the dataset, to fully implement and evaluate an adaptation algorithm, many time-consuming steps are required such as MPD parser design, adaptation logic design and network environment setup. In this paper, a dash simulator which assesses the performance of SVC-based adaptation algorithms without the requirement of any additional implementation steps is proposed. Also, an SVC dataset that includes both CBR and VBR encoded videos is designed. Demonstration is performed as evaluation of an SVC-based adaptation algorithm under several throughput scenarios using the designed dataset. Results show that the proposed system considerably reduces time requirement compared to real-time assessment. Dataset, throughput generation tool and simulator are all publicly available so that the researchers can test their implementation and compare with the results presented in this paper.

In online HPC education platforms, a large amount of educational resources are in the form of video. It is desirable to provide better QoE (Quality of Experience) for students when they are viewing these educational resources. Compared to traditional constant bitrate (CBR) encoding, variable bitrate (VBR) encoding can achieve better video quality and reduce network bandwidth. However, previous adaptive bitrate (ABR) schemes were commonly designed for CBR encoded videos. Such ABR schemes are not suitable to stream VBR encoded videos whose chunk sizes fluctuate rapidly. In this paper, we propose a novel ABR scheme call VBSSR, which takes the characteristics of VBR encoded video into consideration. The basic idea of VBSSR is to stream video chunks with complex scenes at a low bitrate level to reduce bandwidth consumption, and then boost the video quality by leveraging the technique of super-resolution (SR) at the client-side. VBSSR trains a deep reinforcement learning (DRL) based neural model to jointly make bitrate selections and decide which chunks to be enhanced. We conduct extensive trace-driven evaluations to compare VBSSR with other state-of-the-art methods. The experiment results show that our method significantly outperforms existing approaches with improvements in the average video quality by at least 37.1% while reducing the rebuffering time by 24.3%–75.9%.

International audience; Image-Based Rendering allows users to easily capture a scene using a single camera and then navigate freely with realistic results. However, the resulting renderings are completely static, and dynamic effects – such as fire, waterfalls or small waves – cannot be reproduced. We tackle the challenging problem of enabling free-viewpoint navigation including such stationary dynamic effects, but still maintaining the simplicity of casual capture. Using a single camera – instead of previous complex synchronized multi-camera setups – means that we have unsynchronized videos of the dynamic effect from multiple views, making it hard to blend them when synthesizing novel views. We present a solution that allows smooth free-viewpoint video-based rendering (VBR) of such scenes using temporal Laplacian pyramid decomposition video, enabling spatio-temporal blending. For effects such as fire and waterfalls, that are semi-transparent and occupy 3D space, we first estimate their spatial volume. This allows us to create per-video geometries and alpha-matte videos that we can blend using our frequency-dependent method. We also extend Laplacian blending to the temporal dimension to remove additional temporal seams. We show results on scenes containing fire, waterfalls or rippling waves at the seaside, bringing these scenes to life.

Live media streaming (audio and video) technology has boosted up in the recent years. With this, the real time data flow over the network has increased and many algorithms are developed to prevent the congestion of the network. Most of the them are developed for TCP network and only few of them for UDP network. Moreover, these algorithms use programming languages such as Python, Java, etc. which has a vast variety of libraries. This paper highlights an algorithm implemented in C++, to control the bit rate of real-time video over UDP network. The streaming is accomplished with simultaneous execution of image streaming and audio steaming, hence realizing video streaming as a whole. The packets are sent into the network in a manner to attain the required bit rate, which is achieved by varying the size of the buffer at sending device. Total travel time of a single frame is estimated using timestamps and average travel time is calculated. Total travel time along with size of buffer together decides the bit rate. Additionally, required network delay is introduced with the help of timer. It is a simple and stand-alone media streaming process, with highly-reduced complexity. The main aim of the paper is to achieve programmable delay and variable bit rate enabled video streaming for space applications.

The IEEE 802.11ad-based wireless local area network (WLAN) has emerged out as a promising network technology that is capable of operating on millimetre wave spectrum. This network is being used in many applications and can be extended to serve many different streams of the networking industry. However, the network still has some glitches with respect to throughput, overhead, etc., which need to be handled to make it more reliable and efficient. In this paper, distributed directional cooperative medium access control (DD-CoopMAC) protocol for improving variable bit rate (VBR) throughput in IEEE 802.11ad WLAN is proposed. In this protocol, information about network stations is intelligently collected; based on this, the relay stations are selected for transmission. Simulation results illustrate that this protocol aids in enhancing the throughput and reducing the related overhead. Also, simulation results show that the proposed DD-CoopMAC has given high throughput and less MAC delay compared with D-CoopMAC and IEEE 802.11ad.

In this study, an analytical model for the induction machine (IM) with an inter-turn short circuit (ITSC) fault is developed based on the voltage-behind-reactance (VBR) approach. The main motivation is to provide a simulation tool that can be used for fault detection and condition monitoring research. The proposed simulation model is implemented in Simulink, and compared with the outcomes of both finite element analysis (FEA) and experimental studies. The results show the effectiveness of the proposed model as a simple simulation tool for the analysis of IMs with ITSC faults. The proposed Simulink model has been made available as opensource.

In the last years an increasing interest in modeling, design and control of poly-phase machines can be noticed, mainly due to the greater fault tolerance capability of these kind of machines, as well as to the lower prices of electronic switches required in the power drive. This work deals mainly with developing a voltage-behind-reactance (VBR) model for the sixphase symmetrical salient-pole permanent magnet synchronous machine (PMSM) using a simplified space vector approach. This representation eases studying the machine operation under open-phase fault condition. Also, the particularities of the openend machine can be readily simulated using the VBR model. Finally, the behavior of the developed VBR model was tested against the standard representation of the machine’s current mode differential equation.

In the adaptive video streaming scenario, videos are pre-chunked and pre-encoded according to a set of resolution-bitrate/quality pairs on the server-side, namely bitrate ladder. Hence, we propose DeepLadder, which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder by considering video content features, current network capacities, as well as the storage cost. Experimental results on both Constant Bitrate (CBR) and Variable Bitrate (VBR)-encoded videos demonstrate that DeepLadder significantly improvements on average video quality, bandwidth utilization, and storage overhead in comparison to prior work.

Recently, with the development of networks and smart devices, the demand for Ultra High Definition (UHD) video has risen, and HTTP adaptive streaming has attracted attention. HTTP adaptive streaming can guarantee high Quality of Experience (QoE) because it adaptively selects the video quality according to the network state. However, the existing quality control schemes experience unnecessary quality changes and low average video quality due to the bandwidth measurement and the quality control that do not consider the Variable Bit Rate (VBR) content characteristics of the UHD video. In this paper, we propose an adaptive quality control scheme based on VBR content characteristics to improve QoE of UHD streaming service. The proposed scheme measures the bandwidth using the actual bit rate of the segment and the difference in the network adaptability between segments. Furthermore, the proposed scheme defines a quality control region by considering the buffer state of the client. The quality control region consists of four subregions based on buffer thresholds, and the client selects the quality differently according to each subregion. Experimental results have shown that the proposed scheme improves the QoE compared to the existing schemes by minimizing the unnecessary quality changes and maximizing the average video quality.

Background Advances in voice technology have raised new possibilities for apps related to daily health maintenance. However, the usability of such technologies for older users remains unclear and requires further investigation. Objective We designed and evaluated two innovative mobile voice-added apps for food intake reporting, namely voice-only reporting (VOR) and voice-button reporting (VBR). Each app features a unique interactive procedure for reporting food intake. With VOR, users verbally report the main contents of each dish, while VBR provides both voice and existing touch screen inputs for food intake reporting. The relative usability of the two apps was assessed through the metrics of accuracy, efficiency, and user perception. Methods The two mobile apps were compared in a head-to-head parallel randomized trial evaluation. A group of 57 adults aged 60-90 years (12 male and 45 female participants) was recruited from a retirement community and randomized into two experimental groups, that is, VOR (n=30) and VBR (n=27) groups. Both groups were tested using the same set of 17 food items including dishes and beverages selected and allocated to present distinct breakfast, lunch, and dinner meals. All participants used a 7-inch tablet computer for the test. The resulting data were analyzed to evaluate reporting accuracy and time efficiency, and the system usability scale (SUS) was used to measure user perception. Results For eight error types identified in the experiment, the VBR group participants were significantly (P Conclusions Experimental results showed that VOR outperformed VBR, suggesting that voice-only food input reporting is preferable for elderly users. Voice-added apps offer a potential mechanism for the self-management of dietary intake by elderly users. Our study contributes an evidence-based evaluation of prototype design and selection under a user-centered design model. The results provide a useful reference for selecting optimal user interaction design. Trial Registration International Standard Randomized Controlled Trial Registry ISRCTN17335889; http://www.isrctn.com/ISRCTN17335889.

Introduction Computer-aided design and manufacturing of orthodontic retainers from digitally debonded models can be used to facilitate same-day delivery. The purpose of this prospective clinical study was to validate a novel technique for virtual bracket removal (VBR) in-office, comparing the accuracy with 2 orthodontic laboratories that use VBR for retainer fabrication in the digital workflow. Methods The sample consisted of 40 intraoral scans of 20 patients. Four groups were compared. The scans without brackets were used as a control group. VBR was performed by 3 groups: In-office VBR (Software Meshmixer, version 3.5.474; Autodesk, San Rafael, Calif), Orthodent Laboratory (ODL; Buffalo, NY), and New England Orthodontic Laboratory (NEOLab; Andover, Mass). The virtually debonded models were superimposed onto the control models using surface-based registration. Regional 3-dimensional Euclidean distances between surface points of superimposed models were calculated for comparative analysis of surface changes after VBR using Vector Analysis Module (Canfield Scientific, Fairfield, NJ) software. Results The accuracy of VBR using the Meshmixer did not differ significantly from the VBR protocols used by the 2 laboratories. However, there was a statistically significant difference between the 2 laboratories, with ODL showing lower accuracy than NEOLab. Although some differences were statistically significant, they were very small and not considered clinically relevant. There was also a statistically significant difference between the 3 tooth segments (incisors, canines/premolars, and first molars), with VBR of the first molars and second premolars showing the least accuracy. Conclusions The VBR techniques using the in-office Meshmixer, ODL, and NEOLab were considered accurate enough for the clinical use of orthodontic retainers fabricated from printed models.