Variable Bit Rate Video Research Articles

PreSR: Neural-Enhanced Adaptive Streaming of VBR-Encoded Videos With Selective Prefetching

Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.

Exploiting layer and spatial correlations to enhance SVC and tile based 360-degree video streaming

Streaming 360-degree videos over today’s Internet is challenging due to its high bandwidth and low Motion-to-Photon latency requirements. Viewport adaptive streaming is a promising technique to cope with these challenges by allocating more bitrates to a user’s constrained field of view and streaming appropriate representations based on the user’s estimated throughput and fixation point. A typical dynamic adaptive streaming system uses a Media Presentation Description (MPD) file to describe the sustainable bitrates of a period for different quality representations. However, video bitrates vary significantly along the temporal dimension for Variable Bitrate (VBR) videos. In addition to the throughput overestimate/underestimate problem, video bitrate overestimate/underestimate also affects the efficiency of Adaptive Bitrate (ABR) algorithms.In this paper, we first discovered that for SVC and tile-based encoding, strong video chunk size correlation can be observed on both the layer and spatial dimensions for 360-degree videos. We then proposed to enhance the 360-degree video scheduling framework with a chunk size predictor that predicts the size of: (1) a future enhancement layer video chunk based on the corresponding base layer chunk of the same tiled segment; (2) future base layer video chunks based on the horizontally adjacent base layer video chunks. Experiments showed that ABR algorithms augmented by a chunk size predictor can significantly improve a user’s perceived video playback quality.

Reduction of Padding Overhead for RLNC Media Distribution With Variable Size Packets

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.

Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption, and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement that the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL) based approach is employed for the second online power control policy. Via simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared to the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming based online grouped water-filling (GWF) strategy unless the channel is highly correlated.

Review on QoS Provisioning Approaches for Supporting Video Traffic in IEEE802.11e: Challenges and Issues

Recently, the demand for multimedia applications is dramatically increased, which in turn increases the portion of video traffic on the Internet. The video streams, which require stringent Quality of Service (QoS), are expected to occupy more than two-thirds of web traffic by 2019. IEEE802.11e has introduced HCF Controlled Channel Access (HCCA) to provide QoS for delay-sensitive applications including highly compressed video streams. However, IEEE802.11e performance is hindered by the dynamic nature of Variable Bit Rate (VBR) video streams in which packet size and interval time are rapidly fluctuating during the traffic lifetime. In order to make IEEE802.11e able to accommodate with the irregularity of VBR video traffic, many approaches have been used in the literature. In this article, we highlight and discuss the QoS challenges in IEEE802.11e. Then, we classify the existing QoS approaches in IEEE802.11e and we also discuss the selection of recent promising and interesting enhancements of HCCA. Eventually, a set of open research issues and potential future directions is presented.

Adaptive LSTAR Model for Long-Range Variable Bit Rate Video Traffic Prediction

Static bandwidth allocation for variable bit rate (VBR) video traffic forfeits the available bandwidth. Prediction of the next frame size is thus useful in dynamic bandwidth allocation. It has been shown that VBR video traces are long-range dependent, which makes one-frame-ahead prediction insufficient for dynamic bandwidth allocation. Several studies have been conducted based on the linear autoregressive (AR) model to address VBR traffic prediction. In this paper, we propose the use of a nonlinear model from the AR family called logistic smooth transition autoregressive (LSTAR) to predict VBR video traffic. Furthermore, we introduce adaptive algorithms, including least mean square (LMS), normalized LMS (NLMS), kernel LMS (KLMS), and normalized KLMS (NKLMS), to obtain the parameters of the LSTAR model used in long-range VBR traffic prediction. In the proposed model, we do not separate traffic of different frame types and use only one predictor, which results in lower computational complexity. The performance of the proposed predictor for different prediction steps was evaluated and compared with recently introduced predictors. The results indicate that the proposed nonlinear LSTAR-based predictor yields better results than the optimum linear AR predictor, i.e., Wiener–Hopf and others.

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch.

Bit Rate Control for Video Transmission Over Wireless Networks

This paper developed a model of bit- rate control for video streaming over constant bit-rate communication channel, where the MPEG-2, standard Variable Bit-Rate (VBR), is adapted to be used as a Constant Bit Rate (CBR). The targeted image quality is based on the buffer level controlling on the output rate of the video encoder. A CBR transport over networks, result is better performing than that of VBR. It provided flexibility to monitor network and to analyse, where the VBR video streaming over CBR channel can be chosen to remove the problem of congestion. This paper makes the study about the basic problem of adapted VBR-compress the video for transport over a CBR channel. The improved systems are carried using Matlab (Ver 6.5) under Windows XP operating system.

Media-Friendly and TCP-Friendly Rate Control Protocols for Multimedia Streaming

This paper describes a design framework for TCP-friendly and media-friendly rate control algorithms for multimedia streaming applications. The idea of this framework is to start from a TCP-friendly Rate Control (TFRC) transmission rate and then alter this transmission rate so that it tracks the media characteristics of the stream (e.g., bitrate) or other application characteristics like the client buffer fill level. In this way, the media-friendly property of the algorithm is achieved. We give three rules that guide how the TFRC throughput should track the evolution of the stream’s media characteristics and remain TCP-friendly in the long term. We also present, as proof of concept, four simple media-friendly and TCP-friendly congestion control algorithms built using the aforementioned framework. These congestion control algorithms are better suited for multimedia streaming applications than traditional TCP congestion control or smooth congestion control algorithms like TFRC. We have performed evaluations of two of the four proposed media-friendly and TCP-friendly congestion control algorithms under various network conditions and validated that they represent viable transport solutions, better than TFRC, for variable bitrate video streams. More specifically, our two media-friendly and TCP-friendly congestion control algorithms maintained a TCP-friendly throughput in the long term in all experiments and avoided an empty buffer at the client side in situations when TFRC could not achieve this.

Online Source Rate Control for Adaptive Video Streaming Over HSPA and LTE-Style Variable Bit Rate Downlink Channels

Online source rate control (RC) is designed for video streaming over high-speed packet access (HSPA) and Long-Term Evolution (LTE)-style variable bit rate (VBR) downlink channels. The problem is formulated as the adaptive adjustment of the operational mode of a video encoder based on the buffer overflow probability (BOP) feedback received from the radio link control (RLC) layer at the base station (BS). This allows us to maximize the attainable visual quality while keeping the transmitter BOP below a desired threshold and maintaining a video delay as low as possible. We derive an online measurement-based BOP estimation model for the RLC buffer, which is capable of operating with no prior knowledge of the channel variations and of the video characteristics. Based on this estimation model, an online adaptive RC algorithm is proposed to seamlessly adapt the bit stream to the characteristics of VBR channels. Our experiments are conducted in multiuser scenarios using VBR video encoding combined with adaptive modulation and coding (AMC) in the transceiver. The results demonstrate that the proposed source RC regime supports near-instantaneous yet smooth bit stream adaptability, which makes it useful for HSPA and LTE-style systems for the sake of accommodating unknown video traffic characteristics and dynamically fluctuating propagation conditions.

Packet-based Polling Scheme for Video Transmission in IEEE 802.11e WLANs

IEEE 802.11e standard introduces HCF Controlled Channel Access (HCCA) to support quality of service (QoS) for multimedia traffics. In HCCA, the traffics scheduled according to their mean characteristics which favors the Constant Bit Rate (CBR) trans- mission approach. However, it does not efficiently cope with the fluctuation of the Variable Bit Rate (VBR) video streams where the traffic shows deviation from its mean during the traffic lifetime. In this paper, we propose a packet based polling mechanism to enhance the scheduling of pre-recorded VBR video streams in HCCA function. Our approach exploits feedback information about the arrival time of the subsequent video frame obtained through cross-layering approach to accurately schedule the uplink traffics. Simulation experiments reveal the efficiency of the proposed mechanism in providing less delay and high throughput while maintaining medium channel.

A Novel Adaptation Method for HTTP Streaming of VBR Videos over Mobile Networks

Recently, HTTP streaming has become very popular for delivering videos over the Internet. For adaptivity, a provider should generate multiple versions of a video as well as the related metadata. Various adaptation methods have been proposed to support a streaming client in coping with strong bandwidth variations. However, most of existing methods target at constant bitrate (CBR) videos only. In this paper, we present a new method for quality adaptation in on-demand streaming of variable bitrate (VBR) videos. To cope with strong variations of VBR bitrate, we use a local average bitrate as the representative bitrate of a version. A buffer-based algorithm is then proposed to conservatively adapt video quality. Through experiments in the mobile streaming context, we show that our method can provide quality stability as well as buffer stability even under very strong variations of bandwidth and video bitrates.

Optimized Multimedia Streaming and Congestion Control for WLAN-3G Networks

Congestion control of a variable bit-rate video stream over the wireless link is critical issue to assure the quality of the received video. If congestion occurs it leads to the buffer overflows which in turn results in packet loss in the networks. In order to overcome this issue, an Optimized Multimedia Streaming and Congestion Control technique for WLAN-3G Network is proposed. In this study, a two-tier integrated heterogeneous wireless network including WLAN and 3G cellular are considered. The streaming rate is estimated by using Fuzzy Logic congestion (FLC) controller. The Congestion level determination unit determines the congestion level and the rate of change of congestion level based on the inter packet gaps which are taken as input to the FLC. Based on the output of FLC, new streaming rate is estimated. The application adjusts to the new streaming rate in order to maintain the buffer level. Also, the threshold value for buffer level is optimized by using the Greedy Streaming to Threshold and Stop (GSTS) algorithm in order to reduce the streaming monetary cost.

A Pragmatic VBR Stream Scheduling Policy for IEEE 802.11e HCCA Access Method

Nowadays, transferring a variable bit rate (VBR) video streaming on mobile devices is getting popular and necessary. Such traffic streams usually have stringent transferring requirements and in many cases they should be indeed delivered as they were generated. Hence, the IEEE 802.11e hybrid coordination function-controlled channel access (HCCA) access method was proposed to provide better quality-of-service (QoS) requirements for a real-time traffic in the wireless network. However, the reference scheduler of HCCA offered in the standard of the IEEE 802.11e is insufficient for the VBR video stream since it only considers a few characteristics. Hence, choosing the right parameters to meet the QoS requirements of the VBR stream remains an unsolved and opened issue. In this paper, we propose a pragmatic packet scheduling policy for the VBR stream and try to enhance the transmission efficiency for the IEEE 802.11e HCCA access method. The proposed scheduler considers not only traffic characteristics, but also the queue state and the real-time information of the packet. An efficiency analysis is provided to evaluate the transmission efficiency of the proposed scheduler. The result shows that our method can reach higher transmission efficiency than the reference scheduler. Simulations on the transmission of the VBR video stream are also performed to evaluate the performance of the proposed scheme. As it turns out, our design indeed provides a better performance than the reference and other well-known schedulers.

Buffer-Based Adaptive Bitrate Algorithm for Streaming over HTTP

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.

QoE-Based Reduction of Handover Delay for Multimedia Application in IEEE 802.11 Networks

Wireless local area network (WLAN) is becoming increasingly important due to the explosive growth of mobile data traffic. The user in some hot spot areas is usually covered with multiple access points (APs). However, the subsequent challenge is the users' devices need to handover between these APs as the user moves, and it introduces large latency during the handoff process. For multimedia application, quality of experience (QoE) is an essential measurement for network evaluation. But the large handover latency which gives rise to an interruption will greatly degrade the users' QoE to an intolerable extent. Therefore a neighbor list based proactive handoff scheme which has sufficient low handover latency will reduce service disruptions. The proposed scheme exploits the active scan based on information of neighboring APs, received signal strength indicator (RSSI), and variable bitrate video coding (VBR) to reduce handover delay and service interruption. Based on the analysis and simulation, it can be seen that the proposed scheme can decrease the handover time efficiently and increase QoE.

Feasible HCCA Polling Mechanism for Video Transmission in IEEE 802.11e WLANs

IEEE 802.11e standard defines two Medium Access Control functions to support Quality of Service (QoS) for wireless local area networks: Enhanced Distributed Channel Access (EDCA) and HCF Controlled Channel Access (HCCA). EDCA provides fair prioritized QoS support while HCCA guarantees parameterized QoS for the traffics with rigid QoS requirements. The latter shows higher QoS provisioning with Constant Bit Rate traffics. However, it does not efficiently cope with the fluctuation of the Variable Bit Rate (VBR) video streams since its reference scheduler generates a schedule based on the mean characteristics of the traffic. Scheduling based on theses characteristics is not always accurate as these traffics show high irregularity over the time. In this paper, we propose an enhancement on the HCCA polling mechanism to address the problem of scheduling pre-recorded VBR video streams. Our approach enhances the polling mechanism by feed-backing the arrival time of the subsequent video frame of the uplink traffic obtained through cross-layering approach. Simulation experiments have been conducted on several publicly available video traces in order to show the efficiency of our mechanism. The simulation results reveal the efficiency of the proposed mechanism in providing less delay and high throughput with conserving medium channel through minimizing the number of Null-Frames caused by wasted polls.

Moving Object Extraction through a Real-World Variable-Bandwidth Network using KFDA-based RBF

Motion detection has become one of the most important applications in traffic monitoring systems. Video communication in traffic monitoring systems may suffer network congestion or unstable bandwidth over real-world networks with definite bandwidth, which is dangerous in motion detection in video streams of variable-bit-rate. In this paper, we propose a unique Kernel Fisher’s linear discriminant (KFLD)-based radial basis function (RBF) network for motion detection approach for accurate and complete detection of moving objects in video streams of both high and low bit rates. The proposed method will be accomplished through a combination of two stages: pattern generation (PG) and motion extraction (ME). In the PG stage, the variable bitrate video stream properties will be accommodated by this new technique, which subsequently distinguishes the moving objects within the segmented regions belonging to the moving object class by using two devised procedures that is Background Discriminant Procedure and Object Extraction Procedure during the ME stage. The accuracy result evaluations can show that the new method exhibits superior when compared to the old methods.

How variable bitrate video formats can help P2P streaming boost its reliability and scale

P2P streaming is attractive for content providers because it removes the main load of provider's servers. However, just like the conventional streaming, P2P streaming fails to scale. Additionally, P2P streaming suffers from low reliability because network and host performance vary drastically across peers. This paper extends the well-known substream P2P streaming method and proves that variable bitrate formats--the emerging topic in multimedia research can help solve both the scale and reliability problems. The paper analyzes and compares performance among several P2P methods in simulation but also presents a more formal model and optimization framework. The commercial value of this research is in the fact that the proposed P2P delivery model finally allows video content providers to run services globally without procuring local facilities in several countries. The paper also relates to application of the proposal in electronic commerce--distinct from commercial use--by discussing the relevant usecases.

An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring In Real-World Limited Bandwidth Networks

Machine-controlled motion detection technology is Associate in nursing integral element of intelligent transportation systems, and is especially essential for management of traffic and maintenance of traffic police investigation systems. Traffic police investigation systems mistreatment video communication over real-world networks with restricted information measure typically encounter difficulties attributable to network congestion and/or unstable information measure. This is often particularly problematic in wireless video communication. This has necessitated the event of a rate management theme that alters the bit-rate to match the procurable network information measure, thereby manufacturing variable bit-rate video streams. However, complete and correct detection of moving objects beneath variable bit-rate video streams could be a terribly tough task. During this paper, we tend to propose Associate in nursing approach for motion detection that utilizes Associate in nursing analysis - primarily based radial basis perform network as its principal element. This approach is applicable not solely in high bit-rate video streams, however in low bit- rate video streams, as well. The planned approach consists of a varied background generation stage and a moving object detection stage. Throughout the assorted background generation stage, the lower-dimensional Eigen-patterns and also the adjustive background model are established in variable bit -rate video streams by mistreatment the planned approach so as to accommodate the properties of variable bit-rate video streams. Throughout the moving object detection stage, moving objects are extracted via the planned approach in each low bit -rate and high bit- rate video streams; detection results are then generated through the output worth of the planned approach. The detection results created through our approach indicate it to be extremely effective in variable bit-rate video streams over real-world restricted information measure networks. Additionally, the planned methodology will be simply achieved for period of time application. Quantitative and qualitative evaluations demonstrate that it offers blessings over different state- of-the -art ways. For example, and accuracy rates created via the planned approach were up to eighty six.38% and 89.88% beyond those created via different compared ways.