In multiview video systems, multiple cameras generally acquire the same scene from different perspectives, such that users have the possibility to select their preferred viewpoint. This results in large amounts of highly redundant data, which needs to be properly handled during encoding and transmission over resource-constrained channels. In this work, we study coding and transmission strategies in multicamera systems, where correlated sources send data through a bottleneck channel to a central server, which eventually transmits views to different interactive users. We propose a dynamic correlation-aware packet scheduling optimization under delay, bandwidth, and interactivity constraints. The optimization relies both on a novel rate-distortion model, which captures the importance of each view in the 3D scene reconstruction, and on an objective function that optimizes resources based on a client navigation model. The latter takes into account the distortion experienced by interactive clients as well as the distortion variations that might be observed by clients during multiview navigation. We solve the scheduling problem with a novel trellis-based solution, which permits to formally decompose the multivariate optimization problem thereby significantly reducing the computation complexity. Simulation results show the gain of the proposed algorithm compared to baseline scheduling policies. More in details, we show the gain offered by our dynamic scheduling policy compared to static camera allocation strategies and to schemes with constant coding strategies. Finally, we show that the best scheduling policy consistently adapts to the most likely user navigation path and that it minimizes distortion variations that can be very disturbing for users in traditional navigation systems.
Finally, in our scheduling optimization we aim at minimizing the experienced distortion as well as the temporal variations of the experienced distortion for interactive users. Recently, it has been shown the importance of studying the temporal quality variations in adaptive streaming strategies @cite_9 @cite_12 . These works target single view adaptive streaming over HTTP. We follow similar intuitions and extend the mixed objective function composed on both the perceived distortion and the temporal distortion variation to multiview video navigation applications.
@cite_9 @cite_12
2064793449 2013131615
An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network?s transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures max-min distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes. This paper presents a rate-distortion (RD) optimized interactive streaming method for multiview video pre-compressed by H.264 Joint Multiview Video Model (JMVM). In the proposed method, multiple encodings are first used to facilitate the flexible server-client interaction. Second, a RD-optimized scheduling strategy is provided to guarantee the optimal view-dependent delivery of multiview video. In the RD-optimized scheduling strategy, a distortion model is proposed to estimate the expected end-to-end distortion by accounting for both coding and packet-loss-induced distortions, as well as rendering-induced distortion. With the end-to-end distortion model, the server can select the optimal encoding combination for transmission. Experimental results demonstrate that the proposed method can achieve a significant end-to-end RD performance improvement over the selective streaming methods with simulcast coding or scalable multiview coding. In addition, it has better error-resilience performance to combat with packet-losses over the Internet protocol (IP) networks. A continuous evaluation of the end user’s quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http: www.iith.ac.in lfovia downloads.html . 360 videos are becoming more and more popular on video streaming platforms. However, a good quality metric for 360 videos is still an open issue. In this work, we investigate both objective and subjective quality metrics for 360 videos. The goals are to understand the perceived quality range provided by existing mobile 360 videos and, especially, to identify appropriate objective quality metrics for 360 video communications. To that end, a subjective test is conducted in this study. Then, the relationship between objective quality and subjective quality is investigated. Especially, ten objective quality measures are computed, considering the coding distortion measurement, cross-format distortion measurement, and end-to-end distortion measurement. It is found that most of the objective quality measures are well correlated with subjective quality. Also, among the evaluated quality measures, PSNR is shown to be the most appropriate for 360 video communications. It is generally recognized that severe video distortions that are transient in space and or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database. In this paper, a cross-layer adaptation scheme is proposed for quality of service (QoS) provision in the scalable video streaming of high definition (HD) content. The cross-layer parameters, which contain the video rate, payload length of a packet, the mode of modulation and coding scheme (MCS), can be dynamically adapted to minimize distortion of a video streaming under the given delay bound. Based on the channel quality and rate-distortion parameters, the proposed scheme formulates the problem of parameter selection into an optimization problem. Simulation results show that our approach guarantees video quality under QoS constraints. Free viewpoint video (FVV) offers compelling interactive experience by allowing users to switch to any viewing angle at any time. An FVV is composed of a large number of camera-captured anchor views, with virtual views (not captured by any camera) rendered from their nearby anchors using techniques such as depth-image-based rendering (DIBR). We consider a group of wireless users who may interact with an FVV by independently switching views. We study a novel live FVV streaming network where each user pulls a subset of anchors from the server via a primary channel. To enhance anchor availability at each user, a user generates network-coded (NC) packets using some of its anchors and broadcasts them to its direct neighbors via a secondary channel. Given limited primary and secondary channel bandwidths at the devices, we seek to maximize the received video quality (i.e., minimize distortion) by jointly optimizing the set of anchors each device pulls and the anchor combination to generate NC packets. To our best knowledge, this is among the first body of work addressing such joint optimization problem for wireless live FVV streaming with NC-based collaboration. We first formulate the problem and show that it is NP-hard. We then propose a scalable and effective algorithm called PAFV (Peer-Assisted Freeview Video). In PAFV, each node collaboratively and distributedly decides on the anchors to pull and NC packets to share so as to minimize video distortion in its neighborhood. Extensive simulation studies show that PAFV outperforms other algorithms, achieving substantially lower video distortion (often by more than 20–50 ) with significantly less redundancy (by as much as 70 ). Our Android-based video experiment further confirms the effectiveness of PAFV over comparison schemes. Image retargeting algorithms attempt to adapt the image content to the screen without distorting the important objects in the scene. Existing methods address retargeting of a single image. In this paper we propose a novel method for retargeting a pair of stereo images. Naively retargeting each image independently will distort the geometric structure and make it impossible to perceive the 3D structure of the scene. We show how to extend a single image seam carving to work on a pair of images. Our method minimizes the visual distortion in each of the images as well as the depth distortion. A key property of the proposed method is that it takes into account the visibility relations between pixels in the image pair (occluded and occluding pixels). As a result, our method guarantees, as we formally prove, that the retargeted pair is geometrically consistent with a feasible 3D scene, similar to the original one. Hence, the retargeted stereo pair can be viewed on a stereoscopic display or processed by any computer vision algorithm. We demonstrate our method on a number of challenging indoor and outdoor stereo images. Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming. We consider sensor networks that measure spatio-temporal correlated processes. An important task in such settings is the reconstruction at a certain node, called the sink, of the data at all points of the field. We consider scenarios where data is time critical, so delay results in distortion, or suboptimal estimation and control. For the reconstruction, the only data available to the sink are the values measured at the nodes of the sensor network, and knowledge of the correlation structure: this results in spatial distortion of reconstruction. Also, for the sake of power efficiency, sensor nodes need to transmit their data by relaying through the other network nodes: this results in delay, and thus temporal distortion of reconstruction if time critical data is concerned. We study data gathering for the case of Gaussian processes in one- and two-dimensional grid scenarios, where we are able to write explicit expressions for the spatial and time distortion, and combine them into a single total distortion measure. We prove that, for various standard correlation structures, there is an optimal finite density of the sensor network for which the total distortion is minimized. Thus, when power efficiency and delay are both considered in data gathering, it is useless from the point of view of accuracy of the reconstruction to increase the number of sensors above a certain threshold that depends on the correlation structure characteristics. Media streaming over unreliable networks such as the Internet is growing in popularity, but presents unique challenges when trying to get the user experience to be on par with classical mediums such as cable television. These networks have variable network conditions which not only vary between a set of points in the network, but also change over time. In this paper, we present a rate-distortion (R-D) optimized algorithm for adapting the bitrate of streaming media from a chunked encoding, where each chunk is available at multiple bitrates (or quality levels) or is scalable coded. The optimization takes into account the desired startup latency, the desired client buffer size, the current client buffer size, and the estimated network bandwidth. The problem is formulated as a distortion minimization problem subject to multiple rate constraints. For video content, the solution gives a gain of up to 3–4dB in PSNR in difficult portions of the video when compared to commonly used adaptation techniques and can achieve an arbitrarily small desired startup latency. We formulate a system framework for network compression of interactive multi-view streaming video. The setup comprises a media server that delivers the content over two independent network paths to a client. Our system features a proxy-server located at the junction of the wired and wireless portions of each path. The proxy dynamically adapts the content data sent over the wireless links, in response to channel quality feedback from the client, such that video distortion at the client is minimized. We analyze the performance of our system and contrast its characteristics with dynamic content adaptation at the source and conventional streaming architectures, including scalable video. We numerically simulate the operation of all streaming systems under comparison and establish a close agreement between our analysis and the experimental findings. The proposed system delivers superior video quality over the reference competitors, while enabling notable transmission rate savings, at the same time. The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality. We propose a new scheme for streaming media systems that combines adaptive media playout (AMP) with rate-distortion (R-D)optimized packet transmission. AMP, the client-controlled, adaptive modification of the media playout rate, allows us to flexibly adjust the playout deadlines of individual packets, and can therefore reduce reconstruction distortion. This added flexibility incurs a subjective cost, however. We introduce functions that assess the subjective cost of a schedule of playout rate modifications, and we show how to optimize the schedule with respect to these costs and distortion. Because the optimal playout rate schedule and the R-D optimal transmission schedule are interdependent, we solve the two problems jointly. In simulations that model a receiver-driven scenario, results for a short media clip show a more than 2 dB improvement in mean PSNR for R-D optimized transmission scheduling combined with a moderate amount of AMP, over R-D optimized transmission scheduling alone. In this paper, we present error-resilient Internet video transmission using path diversity and rate-distortion optimized reference picture selection. Under this scheme, the optimal packet dependency is determined adapting to network characteristics and video content, to achieve a better trade-off between coding efficiency and forming independent streams to increase error-resilience. The optimization is achieved within a rate-distortion framework, so that the expected end-to-end distortion is minimized under the given rate constraint. The expected distortion is calculated based on an accurate binary tree modeling with the effects of channel loss and error concealment taken into account. With the aid of active probing, packets are sent across multiple available paths according to a transmission policy which takes advantage of path diversity and seeks to minimize the loss rate. Experiments demonstrate that the proposed scheme provides significant diversity gain, as well as gains over video redundancy coding and the NACK mode of conventional reference picture selection. The demand for 360° Virtual Reality (VR) videos is expected to grow in the near future, thanks to the diffusion of VR headsets. VR Streaming is however challenged by the high bandwidth requirements of 360° videos. To save bandwidth, we spatially tile the video using the H.265 standard and stream only tiles in view at the highest quality. The video is also temporally segmented, so that each temporal segment is composed of several spatial tiles. In order to minimize quality transitions when the user moves, an algorithm is developed to predict where the user is likely going to watch in the near future. Consequently, predicted tiles are also streamed at the highest quality. Finally, the server push in HTTP 2 is used to deliver the tiled video. Only one request is sent from the client; all the tiles of a segment are automatically pushed from the server. This approach results in a better bandwidth utilization and video quality compared to traditional streaming over HTTP 1.1, where each tile has to be requested independently by the client. We showcase the benefits of our framework using a prototype developed on a Samsung Galaxy S7 and a Gear VR, which supports both tiled and non-tiled videos and streaming over HTTP 1.1 and HTTP 2. Under limited bandwidth conditions, we demonstrate how our framework can improve the quality watched by the user compared to a non-tiled solution where all of the video is streamed at the same quality. This result represents a major improvement for the efficient streaming of VR videos. While much of multiview video coding focuses on the rate-distortion performance of compressing all frames of all views for storage or non-interactive video delivery over networks, we address the problem of designing a frame structure to enable interactive multiview streaming, where clients can interactively switch views during video playback. Thus, as a client is playing back successive frames (in time) for a given view, it can send a request to the server to switch to a different view while continuing uninterrupted temporal playback. Noting that standard tools for random access (i.e., I-frame insertion) can be bandwidth-inefficient for this application, we propose a redundant representation of I-, P-, and “merge” frames, where each original picture can be encoded into multiple versions, appropriately trading off expected transmission rate with storage, to facilitate view switching. We first present ad hoc frame structures with good performance when the view-switching probabilities are either very large or very small. We then present optimization algorithms that generate more general frame structures with better overall performance for the general case. We show in our experiments that we can generate redundant frame structures offering a range of tradeoff points between transmission and storage, e.g., outperforming simple I-frame insertion structures by up to 45 in terms of bandwidth efficiency at twice the storage cost. Hypertext transfer protocol (HTTP) is the fundamental mechanics supporting web browsing on the Internet. An HTTP server stores large volumes of contents and delivers specific pieces to the clients when requested. There is a recent move to use HTTP for video streaming as well, which promises seamless integration of video delivery to existing HTTP-based server platforms. This is achieved by segmenting the video into many small chunks and storing these chunks as separate files on the server. For adaptive streaming, the server stores different quality versions of the same chunk in different files to allow real-time quality adaptation of the video due to network bandwidth variation experienced by a client. For each chunk of the video, which quality version to download, therefore, becomes a major decision-making challenge for the streaming client, especially in vehicular environment with significant uncertainty in mobile bandwidth. In this paper, we demonstrate that for such decision making, the Markov decision process (MDP) is superior to previously proposed non-MDP solutions. Using publicly available video and bandwidth datasets, we show that the MDP achieves up to a 15x reduction in playback deadline miss compared to a well-known non-MDP solution when the MDP has the prior knowledge of the bandwidth model. We also consider a model-free MDP implementation that uses Q-learning to gradually learn the optimal decisions by continuously observing the outcome of its decision making. We find that the MDP with Q-learning significantly outperforms the MDP that uses bandwidth models. We present a novel client-driven multiview video streaming system that allows a user to watch 3D video interactively with significantly reduced bandwidth requirements by transmitting a small number of views selected according to his her head position. The user's head position is tracked and predicted into the future to select the views that best match the user's current viewing angle dynamically. Prediction of future head positions is needed so that views matching the predicted head positions can be prefetched in order to account for delays due to network transport and stream switching. The system allocates more bandwidth to the selected views in order to render the current viewing angle. Highly compressed, lower quality versions of some other views are also prefetched for concealment if the current user viewpoint differs from the predicted viewpoint. An objective measure based on the abruptness of the head movements and delays in the system is introduced to determine the number of additional lower quality views to be prefetched. The proposed system makes use of multiview coding (MVC) and scalable video coding (SVC) concepts together to obtain improved compression efficiency while providing flexibility in bandwidth allocation to the selected views. Rate-distortion performance of the proposed system is demonstrated under different experimental conditions. We examine the quality of social choice mechanisms using a utilitarian view, in which all of the agents have costs for each of the possible alternatives. While these underlying costs determine what the optimal alternative is, they may be unknown to the social choice mechanism; instead the mechanism must decide on a good alternative based only on the ordinal preferences of the agents which are induced by the underlying costs. Due to its limited information, such a social choice mechanism cannot simply select the alternative that minimizes the total social cost (or minimizes some other objective function). Thus, we seek to bound the distortion: the worst-case ratio between the social cost of the alternative selected and the optimal alternative. Distortion measures how good a mechanism is at approximating the alternative with minimum social cost, while using only ordinal preference information. The underlying costs can be arbitrary, implicit, and unknown; our only assumption is that the agent costs form a metric space, which is a natural assumption in many settings. We quantify the distortion of many well-known social choice mechanisms. We show that for both total social cost and median agent cost, many positional scoring rules have large distortion, while on the other hand Copeland and similar mechanisms perform optimally or near-optimally, always obtaining a distortion of at most 5. We also give lower bounds on the distortion that could be obtained by any deterministic social choice mechanism, and extend our results on median agent cost to more general objective functions.