Bit Rate Video Research Articles

Satisfied-User-Ratio Modeling for Compressed Video.

With explosive increase of internet video services, perceptual modeling for video quality has attracted more attentions to provide high quality-of-experience (QoE) for end-users subject to bandwidth constraints, especially for compressed video quality. In this paper, a novel perceptual model for satisfied-user-ratio (SUR) on compressed video quality is proposed by exploiting compressed video bitrate changes and spatial-temporal statistical characteristics extracted from both uncompressed original video and reference video. In the proposed method, an efficient video feature set is explored and established to model SUR curves against bitrate variations by leveraging the Gaussian Processes Regression (GPR) framework. In particular, the proposed model is based on the recently released large-scale video quality dataset, VideoSet, and takes both spatial and temporal masking effects into consideration. To make it more practical, we further optimize the proposed method from three aspects including feature source simplification, computation complexity reduction and video codec adaption. Based on experimental results on VideoSet, the proposed method can accurately model SUR curves for various video contents and predict their required bitrates at given SUR values. Subjective experiments are conducted to further verify the generalization ability of the proposed SUR model.

An Adaptive Quality Switch-aware Framework for Optimal Bitrate Video Streaming Delivery

Given a large number of online video viewers, video streaming, over various networks, is important communication technology. The multitude of viewers makes it challenging for service providers to provide a good viewing experience for subscribers. Video streaming capabilities are designed based on concepts including quality, viewing flexibility, changing network conditions, and specifications for different customer devices. Adjusting the quality levels, and controlling various relevant parameters to stream the video content with good quality and without interruption is vital. This paper proposes an adaptive framework to balance the average video bitrates with respect to appropriate quality switches, making the transition to higher switches more seamless. The quality adaptation scheme increases the bitrates to the maximum value at their current quality switch before shifting to a higher level. This reduced switching times between levels and guarantees the stability of viewing and avoids interruptions. The use of a dynamic system ensures optimal performance, by controlling system parameters and making the algorithm more tunable. We built the system using an open-source DASH library (Libdash) with QuickTime player, studied the video load changes on two performance parameters, Central Processing Unit and Memory usages that have a high impact on multimedia quality. Consequently, the values of parameters that affected the performance of video streaming could be decreased, permitting users to regulate the parameters according to their preferences. Further, reducing the switching levels will reduce the overloads that occur while transferring from one level to another.

EmuStream—An End-to-End Platform for Streaming Video Performance Measurement

Cord-cutting has spread like wildfire as streaming video become commonplace in the Internet. This motivated intensive research in adaptive video streaming to improve its quality-of-experience (QoE) in the presence of network quality variations. Much of the existing research either employed dummy video contents or open source videos for QoE evaluation which may not capture the full spectrum of characteristics of real-world contents. This work fills this gap by developing a novel EmuStream platform based on real-world contents to enable realistic experiments and evaluation of any adaptive streaming algorithm in any streaming platform. First, EmuStream offers the largest (700+ titles) publicly available video bitrate trace dataset derived from real-world video contents. Second, we developed a mathematical model based on the dataset which can generate bitrate trace data for arbitrary target bitrates without actual video encoding. Third, we developed a novel Virtual Video Generator (VVG) which can generate h.264/h.265-compliant virtual videos that share the same frame/segment sizes as the source videos to enable experiments mimicking the streaming of commercial video contents. Last but not least, we developed a novel Streaming Performance Meter (SPM) that can measure detailed frame-by-frame playback performance from a video recording of the streaming playback of a virtual video. By decoding the virtual video's specially-coded frames, SPM can determine the playback timing and bitrate selected for each frame for use in computing any desired QoE metric. This paper presents the EmuStream platform and validates the trace data estimation model and VVG/SPM tools via controlled experiments.

A Hierarchical Buffer Management Approach to Rate Adaptation for 360-Degree Video Streaming

For the 360-degree virtual reality (VR) videos, to achieve the same user-perceived playout quality with traditional video, the overall video bitrate has to be much higher, which significantly increases the bandwidth requirement. To overcome this issue, most of rate adaptation schemes are proposed based on the users' Field of View (FoV), since users only view part of the entire videos when watching 360-degree videos. However, the FoV prediction is only valid in short terms which incurs low robustness against network dynamics and results in frozen screen events. Since the playback buffer can effectively avoid re-buffering by storing videos in advance, we propose a two-layered hierarchical buffer-based rate adaptation scheme for 360-degree VR videos in this paper. The tiles will be updated to enhance the video quality in the short buffer layer based on FoV prediction, meanwhile the long buffer layer takes charge of downloading new chunks to guarantee the buffer occupancy. To better improve the quality of experience (QoE), we jointly consider the users' FoV, channel conditions and buffer status. Our method achieves a good trade-off between video quality and buffer occupancy. Besides, the maximum buffer level selection is optimized to further improve the performance. The simulation results show that our proposed scheme and algorithms outperform other methods by about 5%-30% of QoE improvement.

Enforcing Bitrate-Stability for Adaptive Streaming Traffic in Cellular Networks

Video streaming over cellular network has become extremely popular in 4G and will be an integral part of future cellular networks. While most modern-day video clients continually adapt quality of video streams, they neither coordinate with network elements nor among each other. Consequently, a streaming client may quickly overload the cellular network, leading to poor Quality of Experience (QoE) for users in the network. Motivated by this problem, we present D-VIEWS — a scheduling paradigm that assures video bitrate stability of adaptive video streams while ensuring better system utilization. D-VIEWS only needs to be aware of the set of video bitrates and requires no changes to streaming clients and other network functions. Through simulations, we also study the performance of proportional fairness scheduler and D-VIEWS in the presence of user arrival and departure events.

Exposing Fake Bitrate Videos Using Hybrid Deep-Learning Network From Recompression Error

Bitrate is generally regarded as an important criterion of video quality. However, with sophisticated video editing software, forgers can create fake bitrate videos by up-converting the bitrate of original videos with lower video quality to attract more viewers on video sharing websites. In this work, we first model the generation process of fake bitrate videos and analyze the dominant sources of information loss. It is found that the recompression error generated by the proposed one-step-further recompression operation is an efficient measurement to expose distinguishable quality variation tendencies between true and fake bitrate videos. Based on this analysis, we propose a detection method for fake bitrate videos using a hybrid deep-learning network from recompression error. For an input video, the patch-wise recompression errors are first calculated to increase the learning capability of the network. To learn robust representations of recompression errors in local regions with different degrees of predictability, a hybrid deep-learning network that contains two branches with heterogeneous structures is designed. For noise-like recompression errors, the first branch has a shallow CNN structure initialized with an Inception-like module using multisize convolutional kernels. For zero-element clustered recompression errors, the second branch has a multi-layer perceptron structure equipped with a unique layer that extracts the histogram of zero-element clustered square regions. The output vectors of different branches are concatenated and then jointly optimized to obtain the patch-wise detection results. Finally, the majority voting (local-to-global) strategy is applied to obtain the final detection result. Extensive experiments are conducted to evaluate the detection performance under various coding parameter settings, such as different bitrates, rate-distortion optimization strategies and so on. The experimental results demonstrate the superiority of the proposed method compared with several state-of-the-art methods to provide more fine-grained forensic clues.

Q2ABR: QoE‐aware adaptive video bit rate solution

SummaryIn this paper, we propose a new adaptive bit rate (ABR) streaming method. This method is based on estimating and monitoring users' video streaming experience, their quality of experience (QoE). This ensures a good user QoE and optimises bandwidth utilisation by monitoring video buffer fill rate to ensure minimal data traffic. First, we achieve a QoE evaluation model based on network bandwidth, video segment representation, and dropped video frame rate parameters. Second, following our QoE evaluation model, we formulate an ABR method using the reinforcement learning (RL) paradigm to select video representations and using a breakpoint detection mechanism to monitor end‐user QoE variation. The proposed ABR method is called “QoE‐aware adaptive bit rate (Q2ABR)” and is composed of three individual modules, one for QoE estimation using machine learning methods, one for QoE variation monitoring using the breakpoint detection mechanism, and one for video representation selection using reinforcement learning. The design objective of Q2ABR is to ensure the overall QoE of these users while maintaining a minimum variation in the standard deviation of the users' QoE values. Third, the performance of the Q2ABR method is evaluated and compared with several existing ABR approaches in the literature using real traces that we collect on different transport scenarios (such as bus and train, among others). Since this method considers the user's perception of video quality as a regulator for optimising the overall video distribution network, good results are ensured in terms of the user's experience and buffer fill rate.

A Semi-fragile Video Watermarking Algorithm Based On Chromatic Residual DCT

In this paper, a semi fragile video watermarking scheme based on chromatic DCT is proposed, which can protect copyright and tamper detection simultaneously. Firstly, through the experiment, it is found that the chroma block has a more stable prediction mode relative to the luminance block, which can effectively reduce the asynchronous probability brought by the change of the prediction mode. At the same time, blocks contains more the number of nonzero (NNZ) coefficients, the less the prediction mode changes. Therefore, for each macroblock, the algorithm sort its 4 × 4 sub macroblock according to the NNZ of chroma DCT coefficients, then select the sub block which has most NNZ residual coefficients. According to the secret key K and medium frequency stability of Intra 4 × 4 coefficients, modulate the relationship of three DCT coefficients near medium frequency. Secondly, the sensitivity of the prediction mode to malicious attacks and recompression operations is different. Therefore, the algorithm classifies the prediction mode of the macroblock and generates the authentication code using the prediction mode. The experimental results show that the video quality is negligible affected after watermark embedding, and the change of video bit rate is basically constant. At the same time, the chromatic DCT algorithm can also use the authentication code of the prediction mode to detect and locate the tampering at the 4 × 4 sub block level with good robustness.

Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud

The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

A consistent QoS routing strategy for video streaming services in SDN networks

SummaryThe software‐defined networking (SDN) paradigm proposes to decouple the control plane (decision‐making process) and the data plane (packet forwarding) to overcome the limitations of traditional network infrastructures, which are known to be difficult to manage, especially at scale. Although there are previous works focusing on the problem of quality of service (QoS) routing in SDN networks, only few solutions have taken into consideration the network consistency, which reflects the adequacy between the decisions made and the decisions that should be taken. Therefore, we propose a network architecture that guarantees the consistency of the decisions to be taken in an SDN network. A consistent QoS routing strategy is then introduced in a way that avoids any quality degradation of prioritized traffic while optimizing resources usage. Thus, we proposed a traffic dispersion heuristic in order to achieve this goal. We compared our approach with several existing framework in terms of best‐effort flows average throughput, average video bitrate, and video quality of experience (QoE). The emulation results, which are performed using the Mininet environment, clearly demonstrate the effectiveness of the proposed approach that outperforms existing frameworks.

Cache-Aware Network Coding-Based Scheduling for SVC Multicast in Wireless Networks with Hard Deadline Constraints

To provide multi-resolution video for heterogeneous users in wireless networks, Scalable Video Coding (SVC) encodes a video bit stream into one base layer and several enhancement layers, on the basis of their contribution in recovering the video correspondingly. However, a decoding dependency is introduced into these layers and artful operation is required for SVC streaming. Meanwhile, each SVC packet is associated with a hard time constraint so that users can recover their desired video quality timely. This paper focuses on the network coding assisted SVC multicast with hard deadline constraints. Specifically, we consider the cache-aware model where users are able to store all received packets and decode out the desired packets when sufficient packets are obtained. We analyze the effect of cached network coded packets on the SVC packets scheduling process and the video quality improvement, and value that effect through assigning proper weight for each packet. We also take multiple factors that affect quality of experience into consideration, such as channel condition, request’s deadline and user’s packets receiving status. Moreover, we adopt the instantly decodable network coding (IDNC) technique to schedule SVC packets. According to packets weighting on the vertices of a well-structured two-layered IDNC graph, an effective heuristic algorithm is proposed to schedule the most valuable SVC packets combinations within acceptable complexity. Extensive simulation results verify the effectiveness and the quality of experience enhancement accomplished by the proposed algorithm.

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks.

Rate-Distortion Optimal Joint Texture and Depth Map Coding for 3-D Video Streaming

For high compression efficiency, 3-D video coding usually employs a multimode methodology to exploit the dependencies between multiple views as well as between texture and depth. However, different coding modes will posses differentiating error propagation behaviour when the compressed 3-D video bit stream is transmitted over packet-switched networks, and thus lead to different amount of visual distortions. Further, the texture and depth distortions are combined in a highly complex fashion to produce the overall view synthesis distortion. To minimize the expected view synthesis distortion, this paper proposes an efficient rate-distortion optimized algorithm for joint selection of texture and depth modes. Firstly, a statistical model is developed to estimate the overall view synthesis distortion, in which the channel distortions caused by error propagation under different coding modes are analyzed. Then, joint optimization of texture and depth modes is derived within an operational rate-distortion framework using the Lagrange multiplier method. The adjacent block dependency caused by warping operation is explicitly considered in optimization, for which we develop a dynamic programming method to find the optimal solution. Finally, we extend the Lagrange minimization method to the more general variable-block-size prediction case, where the optimal quadtree tree structure and the combined coding modes are jointly determined using a multi-level dual trellis. Experimental results are presented for a wide range of packet loss rates to illustrate the effectiveness of the proposed algorithm.

Chunk Duration--Aware SDN-Assisted DASH

Although Dynamic Adaptive Streaming over HTTP (DASH) is the pillar of multimedia content delivery mechanisms, its purely client-based adaptive video bitrate mechanisms have quality-of-experience fairness and stability problems in the existence of multiple DASH clients and highly fluctuating background traffic on the same shared bottleneck link. Varying chunk duration among different titles of multiple video providers exacerbates this problem. With the help of the global network view provided by the software-defined networking paradigm, we propose a centralized joint optimization module-assisted adaptive video bitrate mechanism that takes diversity of chunk sizes among different content into account. Our system collects possible video bitrate levels and chunk duration from DASH clients and simply calculates the optimal video bitrates per client based on the available capacity and chunk duration of each client’s selected content while not invading users’ privacy. By continuously following the background traffic flows, it asynchronously updates the target video bitrate levels to avoid both buffer stall events and network underutilization issues rather than bandwidth slicing, which brings about scalability problems in practice. It also guarantees fair startup delays for video sessions with various chunk duration. Our experiments clearly show that our proposed approach considering diversity of chunk duration and that background traffic fluctuations can significantly provide a better and fair quality of experience in terms of structural similarity--based video quality and startup delay compared to both purely client-based and state-of-the-art software-defined networking--based adaptive bitrate mechanisms.

Performance analysis of video on demand in an IEEE 802.11p-based vehicular network

We consider a VoD (Video on-Demand) platform designed for vehicles traveling on a highway or other major roadway. Typically, cars or buses would subscribe to this delivery service so that their passengers get access to a catalog of movies and series stored on a back-end server. The network infrastructure comprises IEEE 802.11p RSUs (Road Side Units) that are deployed along the highway and deliver video content to traveling vehicles. In this paper, we propose a simple analytical and yet accurate solution to estimate two key performance parameters for a VoD platform: (i) the average download data rate experienced by vehicles over their journey and (ii) the average “interruption time”, which corresponds to the fraction of time the video playback of a given vehicle is interrupted because of an empty buffer. Through multiple examples, we investigate the influence of several parameters (e.g., the video bit rate, the number of vehicles, the distance between RSUs, the vehicle velocity) on these two performance parameters whose outcome may help the sizing of an IEEE 802.11p-based VoD platform.

Delay-aware fountain codes for video streaming with optimal sampling strategy

The explosive demand of on-line video from smart mobile devices poses unprecedented challenges to delivering high quality of experience (QoE) over wireless networks. Streaming high-definition video with low delay is difficult mainly due to (i) the stochastic nature of wireless channels and (ii) the fluctuating videos bit rate. To address this, we propose a novel delay-aware fountain coding (DAF) technique that integrates channel coding and video coding. In this paper, we reveal that the fluctuation of video bit rate can also be exploited to further improve fountain codes for wireless video streaming. Specifically, we develop two coding techniques: the time-based sliding window and the optimal window-wise sampling strategy. By adaptively selecting the window length and optimally adjusting the sampling pattern according to the ongoing video bit rate, the proposed schemes deliver significantly higher video quality than existing schemes, with low delay and constant data rate. To validate our design, we implement the protocols of DAF, DAF-L (a low-complexity version) and the existing delay-aware video streaming schemes by streaming H.264/AVC standard videos over an 802.11b network on CORE emulation platform. The results show that the decoding ratio of our scheme is 15% to 100% higher than the state of the art techniques.

Mathematical Model of a Network Slicing Approach for Video and Web Traffic

Abstract—This paper presents the study of adaptive bitrate control algorithms used to control the quality of the video stream from video surveillance cameras to remote servers through a live wireless network with video and web traffic. In such a scenario, bitrate control algorithms may select an appropriate video bitrate taking the current state of the camera buffers into account. A mathematical model of video streaming is introduced that allows evaluation of the performance of different bitrate control algorithms.

A joint optimization method of coding and transmission for conversational HD video service

Multipath transmission is considered to be a promising approach for supporting data delivery of conversational high-definition (HD) video service as it is able to aggregate more transmission resources, increase the reliability of data transmission, and thereby improve the user experience of service. However, most research achievements of multipath video transmission have mainly concentrated on the efficiency of video delivery while less attention has been paid to the dynamic video bit rate adjustment which is also important to improving the quality of such video service. To take full advantage of multipath video transmission, this paper presents a joint optimization method for conversational HD video service, taking into account the linkage between video coding and transmission. In the aspect of the video coding, a region rate based perceptual coding scheme is applied by the combination usage of the characteristics of such video service, which aims to adapt the output bit rate to match the dynamic network conditions. The scheme allocates different coding frequency or rate for different regions in a video frame according to their perceptual importance dynamically, and then skips some unimportant regions and encodes the key regions. Moreover, a multipath load distribution method is adopted to optimize the transmission process which schedules the coded frames over multiple paths by employing the capacity-limited water filling (CLWF) algorithm, to meet the stringent delay requirements of conversational HD video service. Furthermore, the transmission quality of each path is assessed and the coding parameters are adjusted periodically according to the data transmission status and feedback messages. Experiments are carried out with a simulation in OMNeT++ and the results demonstrate that the proposed method performs much better than the existing schemes in terms of data transmission and playback quality.

Profit Maximization for Video Caching and Processing in Edge Cloud

With the development of communication technology and the explosive growth of video traffic brought by the rapid growth of mobile devices (such as smartphones and wearable devices), great business opportunities have been brought to video service providers. In this paper, we make full use of the cache and computing capacity of edge cloud. Considering the multi bitrate of video, we design the video caching and processing model that offers maximized profit to video service provider. Specifically, we model this problem as the 0-1 optimization problem and design the learning-based online upper confidence bound algorithm based on multi-arm bandit theory. This algorithm can design the corresponding cache and process strategy in real time according to the users' request to video. Furthermore, this strategy can maximize the profit of video provider and satisfy the service quality for users. Finally, experimental results show that our proposed video caching and processing scheme is superior to other schemes.

Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.