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The dissemination of the COVID-19 viruses now extends the usage of video consultations to share perspectives and virtual medical consultations, save expenses and health procedures, track the success of care proposals with detail, consistency, and ease from moment to time. The research aims to study the secur-ity of video consultations. We will also present the advantages and disadvantages of video consultations and the complications of their implementation. This paper mainly proposes a practical, high-efficiency video encoding technique for the new video encoding technique (HEVC) used in video consultations. The technology offered uses the RC5 block encryption algorithm for encrypting the sensitive bits of HEVC with low complexity, short encoding times in real-time implementa-tions, format-compliant HEVC, and constant bitrate. In addition, this paper pre-sents experimental results comparing the proposed HEVC RC5-based selective encryption (SE) algorithm with the other algorithms used in various operating modes using the Advanced Encryption Standard (AES). Also, this paper provides the security analysis of the proposed HEVC RC5-based SE algorithm for video consultation, including;the correlation analysis between the ciphered video and the original one, the histogram of the ciphered video, and the cipher cycle analysis. The security analysis results ensured that the proposed HEVC RC5-based SE algorithm for video consultation is safe and stable.

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.

For a single LTE interface on a mobile bonding router, we study end-to-end delay jitter seen by a constant bit rate (CBR) traffic under the uplink (synchronous non-adaptive) hybrid-automatic repeat request (HARQ)-controlled transport block (TB)-based scheduling. The qualitative behavior of the delay jitter is studied experimentally and it is observed that the delay jitter is not a function of the aggregate CBR traffic generation rate alone, but depends on the CBR burst sizes (in bytes) and inter-burst generation duration separately. An explanation of this behavior is provided using an analytical model that explicitly accounts for LTE’s HARQ and TB concepts. The qualitative behavior of jitter is then used to design an end-to-end adaptation algorithm to achieve a suitable level of delay jitter. We then experimentally study the impact of system parameters (RSSI, Cell ID, device location, RSRQ, RSRP and, importantly, the average TB size) on the delay jitter performance. Applying a standard machine-learning-type classification approach, we find that the average TB size acts as sufficient statistics for determination of delay jitter. The adaptation algorithm is then modified to achieve a better delay jitter performance under significant changes such as serving cell handover.

Device-to-Device (D2D) communication is a novel approach that has been proposed to enhance the performance of traditional cellular networks. This work focuses on video streaming using D2D communications underlaying a Long Term Evolution (LTE) network where resources are allocated to each D2D pair in three modes of operation; cellular, dedicated and reuse. The objective is to maximize the sum-rate of the D2D pairs while maintaining continuous video playback with an acceptable quality level. Unlike previous works, which assign one channel to each user or divide the available resources equally between them or assume constant bit rate streaming (CBR), we estimate the number of Resource Blocks (RBs) needed for all users according to their rate and Signal-to-Noise Ratio (SNR) requirements and consider Scalable Video Coding (SVC). A complete Quality of Experience (QoE)-aware framework for D2D-based video streaming that includes resource allocation, power control, mode selection and SVC layer selection is then proposed while taking into consideration buffer occupancies and the video peak signal-to-noise ratio (PSNR). The efficacy of the proposed scheme is evaluated through extensive simulations and compared to the scenario where only one mode of operation is assigned to all D2D pairs. Furthermore, the effect of the number of D2D users as well as their mobility is also investigated. Simulation results show that the proposed framework improves the throughput of the D2D pairs when compared to a single mode of operation while at the same time providing better video quality and continuity.

In this paper, we present a frame-level constant bit-rate (CBR) control method using recursive Bayesian estimation (RBE) for Versatile Video Coding (VVC). An R - $\lambda $ model for rate control (RC) has handled the total texture and non-texture bits at a time and has worked reasonably well in High Efficiency Video Coding (HEVC). Nevertheless, if the rate estimation is inaccurately performed, that is, the $R$ and $\lambda $ values for a current frame cannot be linearly modeled with their respective values in the previous frames, the resulting RC performance is degraded. In our work, we adopt the RBE which alternates prediction and update steps not only to precisely estimate the rates, but also to allocate target bits based on the changes in the distortions of the previously coded frames, thus considering the rates and distortions simultaneously. Therefore, an elaborate RC can be performed especially at fluctuating frame complexities. Experimental results show that our RC method outperforms the RC of VVC Test Model (VTM-5.0) in terms of normalized root mean square error (NRMSE) with maximum (average) 34.95% (12.35%) improvement, and maintains higher visual quality consistency in terms of standard deviation of PSNR by 33.07% (22.34%) improvement for All Intra (AI), maximum (average) 44.82% (27.29%) and 22.54% (9.50%) for Low Delay (LD), and maximum (average) 47.35% (39.94%) and 30.35% (18.54%) for Random Access (RA), respectively, compared to the default RC method of the original VTM-5.0.

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.

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.

Varieties of wireless communication links, such as HSPA+ access in high speed trains, balloon-based aerial wireless networks and satellite Internet connections have high loss rates. In such environments, Multipath TCP (MPTCP) offers a robust solution compared to regular TCP. However, MPTCP and existing schedulers suffer from performance degradation for both constant bit rate (CBR) and bulk traffic. To tackle this challenge, we develop LAMPS, a novel scheduler for MPTCP, which considers both the loss and delay when selecting subflows, and chooses segments based on subflows’ state. The design goal of LAMPS is to achieve a steady performance for different traffic and significantly reduce the unnecessary bandwidth consumption, especially in case of bursty losses. We have implemented LAMPS and evaluated its performance for Dynamic Adaptive Streaming over HTTP (DASH), CBR, and bulk traffic. Our experiment results show that LAMPS preserves application latency, keeps low memory consumption, and significantly reduces extra bandwidth consumption in the presence of high packet loss rate.

Despite the several speedup methods proposed in the literature, the computational complexity of High Efficiency Video Coding (HEVC) video encoding is still a problem. This paper proposes a fast coding unit (CU) partition decision for use in HEVC encoders based on support vector machine (SVM)-trained offline. The SVM classifiers, features, and training procedures are described in detail, and a justification for the use of SVMs is provided. The trained classifiers are incorporated into a modified reference encoder in the form of a fast CU partition decision algorithm, which decides if the exhaustive search for the best partition is continued or terminated prematurely. Using the proposed method, an average complexity reduction of 48% is achieved with a 0.48% Bjontegaard-Delta bitrate (BD-BR) loss using the random access coding configuration, 44% reduction with a 0.62% BD-BR loss for the Low Delay B, and a 41% reduction with a 0.6% BD-BR loss for the Low Delay P configuration. We also tested our approach under constant bitrate conditions, achieving a 47% reduction in encoding time with a 1.11% loss in the BD-BR. In addition, a decision threshold adaptation is also proposed to allow adjusting the rate-distortion/complexity trade-off of our solution. With this approach, the computational complexity reduction can be varied from 34.9% (with a 0.13% loss in the BD-BR) up to 52.4% (with a 1.11% BD-BR loss) using the random access configuration. Compared with the state-of-the-art solutions, our decision scheme outperforms the related works in terms of combined rate distortion and complexity.

Rapid advances in compression and transmission technologies enabled multi-view video streaming over the Internet through state-of-the-art MPEG standards such as dynamic adaptive streaming over HTTP (DASH) and high-efficiency video coding. Yet, standards for subjective and objective measurement of quality of experience (QoE) are lagging in the area. The MPEG-DASH system requires constant bitrate (CBR) coding and an efficient rate control strategy in order to increase QoE. Multi-view video plus depth format is preferable due to high compression gain but quality of intermediate view is crucial, strictly depending on performance of inter/intra-view rate allocation as well as rendering algorithms. We propose to find optimal ratio for inter-view rate allocation in CBR using our previously developed objective QoE measures using depth maps and structural similarities. To assess small differences and discriminate similar quality levels, a new subjective assessment methodology is also proposed as a multiple stimuli plus simultaneous presentation for paired comparison. The results show that the unequal rate allocation is superior to the equal rate allocation in terms of objective QoE as 0.012–0.04 (which translates into a bit rate saving of 15–24%) and subjective QoE as 0.3–0.5. Furthermore, a very high correlation is achieved between the new QoE measures and paired comparison results.

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.

Mobile streaming media on subways or trains has become a typical and popular scenario in daily life. However, because of the unstable wireless network channel and fluctuations caused by movement, the constant bit rate streaming media brings low-quality service. The adaptive streaming media seems to solve the problems, but the premise is that clients can accurately estimate the network bandwidth. When the client moves at high speed, the irregular fluctuations of network channel make accurate estimation difficult, leading to the low efficiency of the adaptive streaming media. When clients move on subways or trains, the location, speed and the cell load can be known by the wireless network side. With this information, the available network bandwidth can be accurately calculated in advance. Thus, we propose an architecture called Cell-Cluster Network-Assisted Adaptive Media Streaming (CCNA) to optimize the vehicular streaming media over wireless network. We build a cell cluster to introduce the assistance of the wireless network side and estimate the network bandwidth more accurately. The architecture can help clients select the video segment with the highest QoE under the constraint of limited network bandwidth and make the best use of the network bandwidth resources. The experimental results show that compared with traditional adaptive streaming media transmission modes, our architecture improves the average playback bit rate by 32% and the utilization of network bandwidth by 34% at the same time with the least video stalls.

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%.

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.

A Hybrid Differential Manchester-PWM encoding for data transmission and brightness control is implemented in a real time system and successfully integrated to a visible light communication architecture. The proposed digital modulation, allows simultaneous control of brightness maintaining data transmission at constant bit rate. Transmission data is generated using a ZedBoard™ development kit, which has a ARM Cortex-A9 processor and a Artix-7 FPGA. The resulting electrical signal feeds a driver of a commercial white illumination light-emitting diodes (LEDs). We experimentally demonstrate a constant transmission speed of 50Kbps using three 10W white LEDs at a distance of 0.9 meters, with a range of brightness between 10% and 90% of the maximum luminous flux of the LED sources.

This paper proposes a rate control (RC) algorithm for intra-coded sequences (I-frames) within the context of block-based predictive transform coding that departs from using trained models to approximate the rate-distortion (R-D) characteristics of the video sequence. Our algorithm employs piecewise linear approximations of the rate-distortion (R-D) curve of a frame at the block-level. Specifically, it employs information about the rate and distortion of already compressed blocks within the current frame to linearly approximate the slope of the R-D curve of each block. The proposed algorithm is implemented in the High-Efficiency Video Coding (H.265/HEVC) standard and compared with its current RC algorithm, which is based on a trained model. Evaluations on a variety of intra-coded sequences show that the proposed RC algorithm not only attains the overall target bit rate more accurately than the RC algorithm used by H.265/HEVC algorithm but is also capable of encoding each I-frame at a more constant bit rate according to the overall bit budget.\ud

We address the problem of achieving a target end-to-end per-packet delay jitter for an uplink packet traffic from a mobile device to a central server. We provide simple analytical models for predicting the delay jitter and their empirical validation. The structural dependency of the end-to-end per-packet delay jitter on various Constant Bit Rate (CBR) traffic parameters, namely, burst length and inter-burst time, are then utilized to develop a stochastic approximation algorithm to adapt these parameters to achieve a target end-to-end delay jitter. Initial understanding is simulation-based using Network Simulator (NS3). Further, our model is evaluated against the experimental results obtained on a reported real-world implementation.

The recent dramatic increase in video content consumption requires efficient video coding standards, which is specifically true for ultrahigh-definition (UltraHD) resolutions, such as 4K and 8K (i.e., 3840×2160 or 7680×4320 resolutions in terms of luma samples, respectively). The well-known high-efficiency video coding (HEVC) [H.265/Moving Pictures Expert Group (MPEG)-H] standard was approved in 2013. Although HEVC provides approximately 50% coding gain compared to its predecessor advanced video coding (AVC) (H.264/MPEG-4), its adoption is still relatively slow. In addition, larger bitrate savings than those provided by HEVC are currently in demand. At the same time, work on the versatile video coding (VVC) and essential video coding (EVC) standards started in 2018. After intensive development efforts that continued for two and a half years, these two video coding standards have been recently finalized. VVC (H.266/MPEG-I) was developed jointly by the MPEG and the International Telecommunication Union-Telecommunication (ITU-T) Video Coding Experts Group (VCEG). On the other hand, EVC (MPEG-5) is an MPEG-only effort. In this paper, we compare the performance of EVC and VVC, in terms of both coding gains and computational complexity, to their predecessor—the HEVC video coding standard. In addition, given the growing popularity of the AV1 video codec, which was recently developed by the Alliance for Open Media (AOM), we also include AV1 as an alternative baseline and provide corresponding comparison results. According to the experimental results, which have been carried out in a constant bitrate (CBR) mode, EVC provides about 30% bitrate savings compared to HEVC for encoding 4K/2160p entertainment content (such as VoD) in terms of Bjontegaard-delta bitrate (BD-BR) peak-signal-to-noise ratio (PSNR)YUV, while introducing an encoding computational complexity increase of approximately five times. VVC provides larger bitrate savings of about 40% at a price of a significant encoding computational complexity increase of more than nine times. When the performance of HEVC CBR encoding (i.e., with the rate control disabled) was compared to that of AV1 VBR encoding (i.e., with the rate control enabled), it was found that AV1 provides bitrate savings of about 20% compared to HEVC for encoding 4K/2160p video sequences, as a tradeoff of an encoding computational complexity increase by a factor of approximately four. The authors find both EVC and VVC to be very promising successors to HEVC in terms of coding gains and computational complexity, but the jury is still out on the speed of their adoption .

Establishing stable routes over high mobility networks is a challenging field and consequently brought the attention of a lot of current researchers. In this article, a new reactive routing protocol has been proposed which is considered as a major extension of both Ad-hoc On-demand Distance Vector (AODV) and Mobility and Direction Aware AODV (MDA-AODV) prior routing protocols. It is named as Mobility Aware and Dual Phase Ad-hoc On-demand Distance Vector with Adaptive Hello Messages (MA-DP-AODV-AHM). It primarily concentrates on building routes considering vehicles’ speeds and direction of motion with respect to source vehicles, thereby establishing more stable routes and reducing the route breakages. Not only to this extent, but rather, we proposed an adaptive control packet announcement mechanism, which is directly connected to the periodic hello messages approach, with vehicles’ speeds, yielding a significant reduction in the amount of control overhead and congestion over the network. Moreover, the new protocol is proposed to alternate working among two phases, for the reason of guaranteeing the generation of efficient routes on a timely basis, which include MA-DP-AODV-AHM and AODV protocols. Our protocol has been evaluated in terms of control overhead, end-to-end delay, and energy consumption using QualNet simulator version 7.1 in which enormous simulations have been conducted under the base of vehicle density, packet rate, and constant bit rate connections. Simulation results summarize that MA-DP-AODV-AHM contributes efficiently in mitigating the network instability through generating effective stable routes and reducing link failures. Furthermore, it has proved its superiority over AODV and MDA-AODV.

The new High Efficiency Video Coding (HEVC) standard is targeted for high-resolution video contents that are suitable for many high-delay video communications applications, such as video streaming. A few-rate control algorithms (RCAs) have been proposed for the HEVC in the literature. Almost all state-of-the-art RCAs proposed for the HEVC are optimized for constant bit rate low-delay applications. In order to reach a higher visual quality in high-delay applications, such as video streaming, a proportional integral derivative (PID)-fuzzy RCA with the buffering constraint is proposed for the HEVC in this paper. The proposed RCA is designed to minimize fluctuations of quantization parameter (QP) while the buffering constraints are fully obeyed. Unlike conventional fuzzy-PID algorithms that use a PID controller for control and a fuzzy system for tuning the PID parameters, the proposed algorithm uses a fuzzy controller with proportional, integral, and derivative inputs. A base QP is calculated for each group of picture (GOP) by the PID-fuzzy controller and determined QP offsets are added to the base QP to obtain the QP of frames at different temporal layers in the GOP. Experimental results indicate that high rate-distortion performance, fulfilled buffering constraint, and the high visual quality of compressed video are gained simultaneously by the proposed algorithm with low computational complexity.

Video streaming becomes one of the increasing and emerging processes in recent application in computing world. You tube, Face book and etc, are most famous social websites and cloud computing applications are working based on the video streaming. Quality in terms of streaming time is the issue to be considered and solved to satisfy internet customers. In order to obtain this the existing HCCP approach is modified by allocating the optimal station including a controlled constant bit rate streaming. This approach is called as modified controlled channel access scheduling (MCCAS) method. The scheduling process is applied on the key frames over the controlled packet size is generated within a stipulated interval of time. This entire functionality of MCCAS can be applied in video surveillance applications to increase the quality of videos. This MCCAS method is simulated and tested on simulation software as MATLAB and the outcomes are verified. The obtained results show that the proposed MCCAS method obtained fast and quality video streaming than the existing approaches. Also the proposed approach obtained 36.66% of throughput higher than the existing approach and 16 times less time for video transmission in the cloud.

Currently, VR video delivery over 5G systems is still a very complicated endeavor. One of the major challenges for VR video streaming is the expectations for low latency that current mobile networks can hardly meet. Network caching can reduce the content delivery latency efficiently. However, current caching schemes cannot obtain ideal results for VR video since it requests the viewport interactively. In this paper, we propose a tiled scalable VR video caching scheme over 5G networks. VR chunks are first encoded into multi-granularity quality layers, and are then partitioned into tiles to facilitate viewport data access. By accommodating the 5G network infrastructure, the tiles are cooperatively cached in a three-level hierarchal system to reduce delivery latency. Furthermore, a quality-adaptive request routing algorithm is designed to cater for the 5G bandwidth dynamics. Experimental results show that the proposed scheme can reduce the transmission latency over conventional constant bitrate video caching schemes.

At present, the digital video encryption technology has become an interest research topic as a result of very rapid evolution in the application of real-time video over the Internet. So this paper presents a new method for encrypting the selective sensitive data of the latest video coding standard, which called High-Efficiency Video Coding (HEVC). The High-Efficiency Video Coding was founded in 2013 by the Joint Collaborative Team on Video Coding (JCT-VC) from the ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG). The proposed selective encryption HEVC video technique uses the low complexity overhead chaotic logistic map (CLM) to encrypt the sign bits of the Motion Vector Difference (MVD) and the Discrete Cosine Transform (DCT) coefficients in the entropy stage of the process of video encoding. The contribution of the proposed CLM-based HEVC SE is to encrypt the sensitive video bits with the features of low complexity overhead, fast encoding time, keeping the HEVC constant bitrate and format compliant. Also, this paper introduces a comparative study between the proposed CLM-based HEVC SE and the Glenn HEVC SE that uses the Advanced Encryption Standard (AES). Experimental results demonstrate the main feature of the proposed CLM-based HEVC SE, which turned out to save the time of the video encoding with remaining of the near visual distortion of the encrypted video stream by Glenn HEVC SE. This feature is due to the low complexity of the CLM-based encryption employed in the proposed CLM-based HEVC SE scheme instead of using the AES in the Glenn HEVC SE. A course of security investigation experiments is performed for the proposed CLM-based HEVC SE including the main security performance metrics like encryption quality, key space, statistical and sensitivity tests. The achieved test results ensured the superiority of the proposed CLM-based HEVC SE for digital video streams.

Adaptive bitrate streaming is a critical feature in internet video that significantly improves the viewer experience by customizing video stream quality to the viewer device's capability and connectivity. Encoding the source content at multiple quality tiers or bitrates is extremely demanding for post-production houses, studios, and content delivery networks. This paper describes an intelligent multi-bitrate encoder, based on the High Efficiency Video Coding (HEVC)/H.265 standard that encodes a single title to multiple bitrates at significant performance gains and no compression efficiency loss, as compared to standalone single bitrate encoder instances. We first describe the threading infrastructure of x265, and demonstrate its ability to dynamically adapt to varying degrees of parallelism in hardware. We then describe the key architectural design of a multi-bitrate encoder, including thread synchronization challenges across encoder instances. We also discuss the analysis data shared across different quality tiers, that is carefully chosen to eliminate loss of compression efficiency compared to a single bitrate encoder instance. Finally, we show the high performance gains achieved by the multi-encoder, and demonstrate the feasibility of simultaneous encoding to multiple bitrates with negligible loss of compression efficiency.

A novel bitrate model with low complexity is proposed for perceived compression distortion assessment of mobile video with low resolution, which is extremely useful in intermediate network nodes for quality monitoring. Without fully decoding, parameters are extracted by bitstream analysing, such as bitrate, frame type, quantisation parameter, DCT coefficient, motion vector. Bitrate is regarded as an essential parameter meanwhile the bitrate–MOS curve is determined by video content. Respectively, spatial factor is estimated using quantisation parameter and DCT coefficient and temporal factor is estimated using motion vector. Apart from bitrate, the spatial and temporal factors, which reflect the characteristic of video content, are considered in the proposed model to obtain a more accurate evaluation. Experimental results show that the overall performance of proposed model significantly outperforms that of the other five bitrate models in terms of widely used performance criteria, including the Pearson correlation coefficient (PCC), the Spearman rank-order correlation coefficient (SROCC), the root-mean-squared error (RMSE) and the outlier ratio (OR).

In this paper, we propose a selective encryption scheme with a chaotic encryption system for the High Efficiency Video Coding (HEVC) standard in which Context Adaptive Binary Arithmetic Coding (CABAC) is the only entropy coder for transform coefficient coding. Our method focuses on the binstrings of truncated rice with a context “p” (TRp) code suffix and kth order Exp-Golomb (k = p + 1) code suffix before Binary Arithmetic Coding (BAC) for the remaining absolute level coding, which is coded by the bypass mode in the entropy coding stage. The probability of symbols does not change and CABAC decoding has no effect after encryption. Several different YUV sequences are used for experimental evaluation of the proposed algorithm. Compared to previous researches, our approach has good protection for video information, which keeps a constant bitrate and format compatibility, and meantime it has a negligible impact on encoding performance.

Video quality is analysed by packetization, packet loss, and codec.The paper goes beyond prior studies as it tests SD, HD, 3D, and 4kUHD video.Simulations and network experiments include VBR, CBR, H.264 and HEVC streaming.The paper forecasts bitrates and expected quality in error-prone environments.The impact of packet reordering and duplication is also considered. The trend towards video streaming with increased spatial resolutions and dimensions, SD, HD, 3D, and 4kUHD, even for portable devices has important implications for displayed video quality. There is an interplay between packetization, packet loss visibility, choice of codec, and viewing conditions, which implies that prior studies at lower resolutions may not be as relevant. This paper presents two sets of experiments, the one at a Variable BitRate (VBR) and the other at a Constant BitRate (CBR), which highlight different aspects of the interpretation. The latter experiments also compare and contrast encoding with either an H.264 or an High Efficiency Video Coding (HEVC) codec, with all results recorded as objective Mean Opinion Score (MOS). The video quality assessments will be of interest to those considering: the bitrates and expected quality in error-prone environments; or, in fact, whether to use a reliable transport protocol to prevent all errors, at a cost in jitter and latency, rather than tolerate low levels of packet errors.

Information hiding based on bitrate switching is a novel steganographic scheme for voice over IP, which can resist traditional steganalysis methods. However, the state-of-the-art method has not achieved its best embedding performance. Thus, in this letter, we present two adaptive bitrate modulating-based steganographic methods introducing Hamming matrix encoding (HME) strategy. The first one can conceal ${\lfloor \log _{2}(n+1)\rfloor }$ bits of secret data into ${n}$ speech frames with at most one bitrate switching by applying the HME strategy to each frame group, while the second one, compared with the state-of-the-art method, can embed ${(1+\lfloor \log _{2}(n+1)\rfloor)/2}$ bits of secret data into ${n}$ speech frames without increasing the number of bitrate switches by only employing the HME strategy for the frame groups with bitrate switching. The theoretical analysis and experimental evaluation demonstrate that the proposed methods are feasible, and outperform the existing one on both covert bandwidth and embedding efficiency while achieving the comparably good steganographic transparency.

In this paper, we evaluate the performance of two WMN architectures (infrastructure/backbone WMNs (I/B WMNs) and Hybrid WMN architectures) considering throughput, delay, jitter and fairness index metrics. For simulations, we used ns-3 and optimized link state routing. We compare the performance of distributed coordination function and enhanced distributed channel access (EDCA) for exponential and Weibull distributions of mesh clients by sending multiple constant bit rate flows in the network. The simulation results show that for exponential distribution, in case of Hybrid WMN, the throughput of both MAC protocols is higher than I/B WMN. For 30 flows, the throughput of EDCA in case of hybrid WMN is about 1.4 times higher than I/B WMN. For Weibull distribution, in case of I/B WMN, the throughput of both MAC protocols is higher than Hybrid WMN. For 30 flows, the throughput of EDCA in case of I/B WMN is about 2.6 times higher than Hybrid WMN. For exponential distribution, the delay and jitter of Hybrid WMN are lower than I/B WMN. For 20 flows, the delay of EDCA in case of hybrid WMN is about 10 times lower than I/B WMN. For Weibull distribution, the delay and jitter of both architectures are almost the same. However, for 10 flows, the delay of I/B WMN is lower compared with hybrid WMN. For exponential distribution, the fairness index of I/B WMN is a little bit higher than hybrid WMN. For Weibull distribution, the fairness index is almost the same for both WMN architectures.

A method is introduced to enhance the High Efficiency Video Coding early skip decision process. The proposed method exploits visual acuity and high-velocity motion perception of the human visual system (HVS). The method determines coding units where the HVS is unlikely to notice the changes and hence can be skipped. In this paper, the focus is mobile devices, such as smart phones, where displays are less than 5 in diagonal length and bandwidth availability is low. The proposed method exploits the mismatch between the video resolution, display resolution, and visual acuity of users. This method has very low impact on subjective quality while reducing encoding time for low bitrate environments. The proposed method reduces both encoding time and bitrate; the results show an average of 21.7% decrease in encoding time, 13.4% decrease in average bitrate, and very low impact on subjective quality ratings. The proposed methods can be adapted to decrease encoding time and bitrate in devices with lower resolution displays.

The 802.11e Task Group has established to enhance Quality of Service (QoS) provision for time-bounded services in the current IEEE 802.11 Medium Access Control (MAC) protocol. The QoS is introduced throughout Hybrid Coordination Function Controlled Channel Access (HCCA) for the rigorous QoS provision. In HCCA, the station is allocated a fixed Transmission Opportunity (TXOP) based on its TSPEC parameters so that it is efficient for Constant Bit Rate streams. However, as the profile of Variable Bit Rate (VBR) traffics is inconstant, they are liable to experience a higher delay especially in bursty traffic case. In this paper, we propose an integrated solution called Adaptive Feedback-based HCCA (AF-HCCA) for supporting video stream that shows the variability on both packet size and generation interval. The AF-HCCA improves both polling and TXOP assignment mechanisms which efficiently schedules the TS based on their actual need. Simulation experiments have been conducted to show that the proposed scheduler not only enhances the packet delay, but also improves the channel utilization by mitigating the over-pollling issue.

This paper describes a real-time 4K/60p High Efficiency Video Coding (HEVC, the latest coding standard) multi-bitrate encoding system with content-aware bitrate control. Our encoding system has multiple encoding core LSIs that can each encode 4K/60p or up to HD x 4 streams. To reduce streaming bandwidth consumption, the encoding channels adopt dynamic seamless bitrate control, which is based on content aware bitrate control and supports multi- channel real-time encoding for adaptive bitrate streaming. Experimental results show the system reduces bitrates obtained with constant bitrate encoding by as much as 56.7%.

This paper discusses the background, challenges, opportunities for parallelization of low-latency video transport of JPEG XS streams over the real-time network protocol (RTP). Transport of compressed video signals over a constant bitrate channel requires smoothing buffers at encoder and decoder side. To limit their sizes in partical implementations, part 2 of the JPEG XS standard defines a normative buffer model along a sequential hypothetical reference decoder. Due to its sequential nature, it cannot be directly applied to high-speed applications that necessarily depend on multithreading. In this paper, the JPEG XS Part 2 buffer model is introduced, then the current state of JPEG XS RTP transport standardization is discussed, and multiple strategies for multithreaded encoding under latency contraints within this application are reported and analyzed.

This paper describes optical network technologies to accommodate various types of base stations for 5G and beyond. AMCC technique for WDM-PON suitable for constant bit rate stream and cooperative I/F and DBA to achieve low-latency transmission with TDM-PON are introduced.

The need for sensors to deliver, communicate, collect,\ud alert, and share information in various applications has\ud made wireless sensor networks very popular. However, due to\ud its limited resources in terms of computation power, battery\ud life and memory storage of the sensor nodes, it is challenging\ud to add security features to provide the confidentiality, integrity,\ud and availability. In order to communicate reliably with trust and\ud authenticity, providing data and system security especially for\ud those sensors dealing with sensitive data related to healthcare,\ud military activity, environmental sensing for weather prediction or\ud seismic data etc. is vital. Blockchain technology ensures security\ud and avoids the need of any trusted third party for security.\ud However, applying Blockchain in a resource-constrained wireless\ud sensor network is a challenging task because Blockchain is\ud power, computation, and memory hungry in nature and demands\ud heavy bandwidth due to control overheads. In this paper, a new\ud routing and a private communication Blockchain framework\ud is designed and tested with sensors generating constant and\ud continuous data (like voice and video). However, it is realized\ud that even if computation and bandwidth requirements are taken\ud for granted, storage and battery life will cripple the sustainability\ud of Blockchain application in sensor networks especially for high\ud data generating sensors. The proposed Load Balancing Multi-\ud Hop (LBMH) routing shares and enhances the battery life of\ud the Cluster Heads and reduce control overhead during Block\ud updates, but due to limited storage and energy of the sensor\ud nodes, Blockchain in sensor networks may never be a reality\ud unless storage and battery life of sensor devices are not limited on\ud the one hand and computation power and bandwidth availability\ud are high, on the other.

The Internet of Things (IoT) devices can be identified and controlled intelligently irrespective of its location, across the world to develop more easiness to human lives. The IoT devices constantly forward a large amount of data towards sink nodes which requires extensive routing. Thus, routing is an important aspect of the IoT. This paper presents a performance analysis of two well-known routing protocols: (a) Destination Sequenced Distance Vector routing (DSDV), and (b) Ad hoc On Demand Distance Vector routing (AODV) in the Vehicular Ad hoc network (VANET) based IoT environment. Here, we propose a new scenario in which Constant Bit Rate (CBR) sources increases incrementally and reaches half out of the total nodes whereas the other nodes which are kept stationary decreases exponentially. The total number of nodes in the topology remains constant. The CBR source nodes move with variable speed to different waypoints as destinations at different times. The performance of above- mentioned protocols has been analyzed in our new proposed scenario, keeping in view the TCP Reno and UDP. The comparison among these two routing protocols has been done on the basis of delay, jitter, packet dropped ratio, throughput transferred and generated throughput.

Much of the incremental improvement to TCP over the past three decades had the ultimate goal of making it more effective in using the long-fat pipes of the global Internet. This resulted in a rigid set of mechanisms in the protocol that put TCP at a disadvantage in small-delay environments such as data centers. In particular, in the presence of the shallow buffers of commodity switches and the short round trip times in data centers, the continued use of a large TCP initial congestion window and a huge minimum retransmission timeout (both inherited from the Internet-centric design) results in a very short TCP loss cycle that affects particularly the flow completion times of short-lived incast flows. In this paper, we first investigate empirically the TCP loss cycle and discuss its impact on packet losses, recovery and delay; then we propose a switch-based congestion controller with hysteresis (HSCC) that aims to stretch the TCP loss cycle without modifying TCP itself. To protect incast flows from severe congestion, HSCC is designed to transparently induce the TCP source to alternate between its native TCP congestion control algorithm and a slower more conservative constant bit rate flow control mode that is activated when congestion is imminent. We show the stability of HSCC via analytical modelling, and demonstrate its effectiveness via simulation and implementation in a small testbed.

Available distributed video coding codecs are mostly based on decoder rate control scheme where the parity bits for decoding can be achieved over a feedback channel. Meanwhile, the frequent requests over feedback channel increase the transmission delay. The feedback-free distributed video coding, relying on encoder rate control in literatures, has overcome the aforementioned shortcoming. However, when performing parity bitrate estimation and other operations, the feedback-free distributed video coding systems based on bit-plane usually require high precision of bitrate estimation and high quality of side information at the encoder. In this paper, we propose a frame-level distributed video coding system based on encoder rate control. The innovations include three parts: 1) an adaptive coding mode selection algorithm is proposed, which utilizes both temporal and spatial correlation and reduces the complexity of encoder; 2) a bit-plane rearrangement method is adopted, which makes the coding rate on each bit-plane homogeneous and effectively reduces the accuracy requirement of the parity bitrate prediction and improves the efficiency of rate estimation; 3) a frame-level parity bitrate estimation scheme is presented to enhance the efficiency of rate estimation on the basis of a look-up table. Numerical results verify that the proposed scheme remarkably improves the rate distortion performance of distributed video coding at low bitrate.

This paper describes a theoretical framework for rate allocation to maximize the power efficiency of overall heterogeneous wireless networks whose users are assumed to have multihoming capabilities. Therefore, the paper first presents a power consumption model considering the circuit power and radio transmission power of each wireless network. Using this model, two novel power efficient rate allocation schemes (PERAS) for multihoming services are proposed. In this paper, the convex optimization problem for maximizing the power efficiency over wireless networks is formulated and solved while guaranteeing the required quality of service (QoS). Here, both constant bit rate and variable bit rate services are considered. Furthermore, we extend our theoretical framework by considering the sleep-operation management of wireless networks. The performance results obtained from numerical analysis reveal that the two proposed schemes offer superior performance over the existing rate allocation schemes for multihoming services and guarantee the required QoS.

Many HTTP streaming video systems have been developed and widely deployed in recent years. Previous efforts were mainly spent on improving the caching of videos or proposing mid-stream measurement methods to update the best bitrate. However, since the video length is often short, the mid-stream measurement may not even converge to the best bitrate due to insufficient bandwidth estimates. On the other hand, because of diversified Web infrastructure, estimating the actual network quality at the pre-stream stage is increasingly challenging for video service providers. In this paper, we propose IRate, which enables video service providers to proactively profile clients’ streaming performance by carrying out pre-stream measurement in the Content Delivery Network (CDN). With the measurement results, the video stream can start at the best video quality at the onset of streaming. This is especially beneficial to short video clips, which are very popular in the Internet today. IRate is composed of a probe kit and a quality oracle. The probe kit utilizes the pre-stream time window (e.g., user’s think time and pre-roll advertisement) for measuring network quality by running a lightweight measurement script on the Web page to induce probe packets from the IRate middlebox on the server side. With the measurement results, the quality oracle estimates the clients’ streaming performance by determining the highest initial bitrate with a pre-trained decision tree. Our testbed results show that IRate is able to achieve 80% accuracy in determining the bitrate within 10s. By having a better estimate of the best initial bitrate, the buffering time and rebuffering events are significantly reduced in HTTP streaming. Furthermore, the stability and the efficiency in dynamic adaptive streaming over HTTP streaming are also improved by about 40% and 36%, respectively. Our user quality of experience (QoE) experiment further validates that IRate can improve the QoE by more than 6% and the perceived quality of initial quality by 24% in the actual Internet environment.

Concomitant with rapid advancements in video streaming technology, concurrence video traffic has increased significantly on communication channels. Conflicts often arise among the various video streams on these communication channels when the available bandwidth is limited because the bitrates and transmission range required often vary. This study proposes a server-side-based rate allocation algorithm for content delivery networks (CDNs). Instead of simply considering bitrate selection from the perspective of network and client conditions, the algorithm combines user experience with video bitrate allocation to maximize viewer engagement. First, the values of users are evaluated and a user value computation method is proposed. Second, we developed a profit maximization bitrate allocation approach (PMBAA) that enables both content providers and CDNs to maximize profits by providing guaranteed video quality. At last, the results of test bed experiments and analyses verify that PMBAA enables high-value clients to obtain more preferable bitrates than the HTTP live streaming algorithm developed by Apple Inc.

Multi-view/3D video is currently available in games, entertainment, education, security, and surveillance applications . Since the amount of data in multi-view/3D increases proportionally with the number of cameras, and due to different bandwidth and playback capabilities of receivers, appropriate compression of multi-view/3D video to produce the correct bitrate while maintaining smooth video quality is crucial, a task that is mostly performed by the rate control module of the encoder. There are many existing rate control algorithms for single-view and multi-view video coding considering the specific features or aspects of these videos. In this paper, we introduce a novel view-level rate distortion (RD) model. We use a systematic methodology to derive this RD model by investigating the impact of multi-view/3D video characteristics on the bitrate of a compressed video. Our proposed RD model considers the concepts of intra-view and inter-view disparity as an effective feature of multi-view/3D video to estimate the overall bitrate of each view more accurately. Evaluation results indicate that our proposed view-level RD model outperforms existing linear models by a factor of 3 and can predict the rate of each view with relatively high precision and a low estimation error of 12% on average.

Adaptive Bitrate (ABR) video streaming is widely used for over-the-top (OTT) video delivery. Recently, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, spurred by the potential of improving user QoE (Quality of Experience) and reducing network bandwidth requirements compared to Constant Bitrate (CBR) encodings. However VBR introduces new challenges for ABR streaming, whose nature and implications are little understood. We explore these challenges across diverse video genres, encoding technologies, and platforms. We identify distinguishing characteristics of VBR encodings that impact user QoE and should be factored in any ABR adaptation decision. Traditional ABR adaptation strategies designed for the CBR case are not adequate for VBR. We develop novel best practice design principles to guide ABR rate adaptation for VBR encodings. As a proof of concept, we design a novel and practical control-theoretic rate adaptation scheme, CAVA (Control-theoretic Adaption for VBR-based ABR streaming), incorporating these concepts. Extensive evaluations show that CAVA substantially outperforms existing state-of-the-art adaptation techniques, validating the importance of these design principles.

Image quality assessment (IQA) has attracted more and more attention due to the urgent demand in image services. The perceptual-based image compression is one of the most prominent applications that require IQA metrics to be highly correlated with human vision. To explore IQA algorithms that are more consistent with human vision, several calibrated databases have been constructed. However, the distorted images in the existing databases are usually generated by corrupting the pristine images with various distortions in coarse levels, such that the IQA algorithms validated on them may be inefficient to optimize the perceptual-based image compression with fine-grained quality differences. In this paper, we construct a large-scale image database which can be used for fine-grained quality assessment of compressed images. In the proposed database, reference images are compressed at constant bitrate levels by JPEG encoders with different optimization methods. To distinguish subtle differences, the pair-wise comparison method is utilized to rank them in subjective experiments. We select 100 reference images for the proposed database, and each image is compressed into three target bitrates by four different JPEG optimization methods, such that 1200 distorted images are generated in total. Sixteen well-known IQA algorithms are evaluated and analyzed on the proposed database. With the devised fine-grained IQA database, we expect to further promote image quality assessment by shifting it from a coarse-grained stage to a fine-grained stage. The database is available at: https://sites.google.com/site/zhangxinf07/fg-iqa.

Video data traffic is moving from Broadcast to Internet (e.g. OTTTV) distribution. Related economic models are different. It requires to review the rate control paradigm moving from Constant Bitrate (CBR) model to Variable Bitrate (VBR) model using a capped bitrate constraint. In this paper, we propose a Constant Quality Control (CQC) algorithm which minimizes the bitrate under constraints of a target video quality level and a capped bitrate. CQC algorithm relies on a R-D model which considers the temporal distortion propagation along a video sequence. Introducing a D(Q) model, and a capped bitrate constraint, the optimal local quantizers are computed to fulfill a target level of distortion. Consistency and R-D performances of the proposed solution are verified in the context of HEVC. It meets the target level of quality with an average deviation of 6.9% while reducing bitrate of −7.6% in average compared to constant QP mode. CQC algorithm is particularly well adapted to the OTT-TV distribution, where the Adaptive Bitrate (ABR) mechanism is built on top of quality levels.

Multipath TCP is a recent TCP extension that enables the usage of multiple networks for a single connection. Since September 2017, Apple has enabled Multipath TCP on iOS11 for all applications. The main dedicated use case of multipath usage resides in network resiliency. We evaluate how the "interactive" mode of Multipath TCP behaves under user mobility scenarios using bidirectional constant bit rate traffic. Our results show that the passage from WiFi to cellular is not abrupt, justifying the usage of multiple paths. However, there is still some room for improvement for Multipath TCP in such cases.