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Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting multiple high-quality camera views, which can be used to synthesize additional virtual views to offer a smooth navigation. However, when network resources are limited, not all camera views can be sent at high quality. It is therefore important, yet challenging, to find the right tradeoff between coding artifacts (reducing the quality of camera views) and virtual synthesis artifacts (reducing the number of camera views sent to users). To this aim, we propose an optimal transmission strategy for interactive multiview HTTP adaptive streaming. We propose a problem formulation to select the optimal set of camera views that the client requests for downloading, such that the navigation quality experienced by the user is optimized while the bandwidth constraints are satisfied. We show that our optimization problem is NP-hard, and we therefore develop an optimal solution based on the dynamic programming algorithm with polynomial time complexity. To further simplify the deployment, we present a suboptimal greedy algorithm with effective performance and lower complexity. The proposed controller is evaluated in theoretical and realistic settings characterized by realistic network statistics estimation, buffer management, and server-side representation optimization. Simulation results show significant improvement in terms of navigation quality compared with alternative baseline multiview adaptation logic solutions. [ABSTRACT FROM AUTHOR]

Dynamic adaptive streaming addresses user heterogeneity by providing multiple encoded representations at different rates and/or resolutions for the same video content. For delay-sensitive applications, such as live streaming, there is however a stringent requirement on the encoding delay, and usually the encoding power (or rate) budget is also limited by the computational (or storage) capacity of the server. It is therefore important, yet challenging, to optimally select the source coding parameters for each encoded representation in order to minimize the resource consumption while maintaining a high quality of experience for the users. To address this, we propose an optimization framework with an optimal representation selection problem for delay, power, and rate constrained adaptive video streaming. Then, by the optimal selection of source coding parameters for each selected representation, we maximize the overall expected user satisfaction, subject not only to the encoding rate constraint, but also to the delay and power constraints at the server. We formulate the proposed optimization problem as an integer linear program formulation to provide the performance upper bound, and as a submodular maximization problem with two knapsack constraints to develop a practically feasible algorithm. Simulation results show that the proposed weighted rate and power cost benefit greedy algorithm is able to achieve a near-optimal performance with very low time complexity. In addition, it can strike the best tradeoff both between the rate and power cost, and between the algorithm’s performance and the delay requirements proposed by delay sensitive applications. [ABSTRACT FROM AUTHOR]

Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies have received a lot of attention in the design of medium access control protocols. In this work, we consider generic systems where sources in different importance classes compete for a common channel. We propose a new prioritized IRSA algorithm and derive the probability to correctly resolve collisions for data from each source class. We then make use of our theoretical analysis to formulate a new optimization problem for selecting the transmission strategies of heterogenous sources. We optimize both the replication probability per class and the source rate per class, in such a way that the overall system utility is maximized. We then propose a heuristic-based algorithm for the selection of the transmission strategy, which is built on intrinsic characteristics of the iterative decoding methods adopted for recovering from collisions. Experimental results validate the accuracy of the theoretical study and show the gain of well-chosen prioritized transmission strategies for transmission of data from heterogenous classes over shared wireless channels. [ABSTRACT FROM AUTHOR]

We consider a multiuser OFDM system in which users want to transmit videos via a base station. The base station knows the channel state information (CSI) as well as the rate distortion (RD) information of the video streams and tries to allocate power and spectrum resources to the users according to both physical layer CSI and application layer RD information. We derive and analyze a condition for the optimal resource allocation solution in a continuous frequency response setting. The optimality condition for this cross layer optimization scenario is similar to the equal slope condition for conventional video multiplexing resource allocation. Based on our analysis, we design an iterative subcarrier assignment and power allocation algorithm for an uplink system, and provide numerical performance analysis with different numbers of users. Comparing to systems with either only physical layer or only application layer information available at the base station, our results show that the user capacity and the video PSNR performance can be increased significantly by using cross layer design. Bit-level simulations which take into account the imperfection of the video coding rate control, the variation of RD curve fitting, as well as channel errors, are presented. [ABSTRACT FROM AUTHOR]

Coding and diversity are very effective techniques for improving transmission reliability in a mobile wireless environment. The use of diversity is particularly important for multimedia communications over fading channels. In this work, we study the transmission of progressive image bitstreams using channel coding in a 2-D time-frequency resource block in an OFDM network, employing time and frequency diversities simultaneously. In particular, in the frequency domain, based on the order of diversity and the correlation of individual subcarriers, we construct symmetric n-channel FEC-based multiple descriptions using channel erasure codes combined with embedded image coding. In the time domain, a concatenation of RCPC codes and CRC codes is employed to protect individual descriptions. We consider the physical channel conditions arising from various coherence bandwidths and coherence times, leading to a range of orders of diversities available in the time and frequency domains. We investigate the effects of different error patterns on the delivered image quality due to various fade rates. We also study the tradeoffs and compare the relative effectiveness associated with the use of erasure codes in the frequency domain and convolutional codes in the time domain under different physical environments. Both the effects of intercarrier interference and channel estimation errors are included in our study. Specifically, the effects of channel estimation errors, frequency selectivity and the rate of the channel variations are taken into consideration for the construction of the 2-D time-frequency block. We provide results showing the gain that the proposed model achieves compared to a system without temporal coding. In one example, for a system experiencing flat fading, low Doppler, and imperfect CSI, we find that the increase in PSNR compared to a system without time diversity is as much as 9.4 dB. [ABSTRACT FROM AUTHOR]

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise. [ABSTRACT FROM AUTHOR]

Wireless technology is a revolutionary advancement providing users with ubiquitous data and telephony access anywhere and anytime with no physical (PHY) connection. The currently deployed wireless networks like Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), Universal Mobile Telecommunications Systems (UMTS), and Long Term Evolution (LTE) systems have different characteristics that make them complementary in terms of performance, coverage, and cost. This network variety presents an opportunity to provide better services to end users, given the advances in mobile terminals (MTs). To reach this goal, an appropriate automatic network selection (ANS) mechanism, which is able to always select the best access network, is needed. This consists of constantly monitoring any type of available access network, i.e., automatically selecting and switching to the best one, as the network that maximizes the users quality of experience, taking into account their preferences and the terminal and network conditions. ANS is a multidimensional decision-making problem that can be solved by finding an appropriate complex tradeoff between possibly conflicting criteria. In this paper, we propose an analytical model to capture the preferences of end users. Based on this model, we design an ANS mechanism that takes into account all aspects of the tradeoff between the quality of the connections, the preferences of the end users, and the cost. To highlight the benefits of our approach from the perspectives of both end users and network operators, we have implemented and tested the solution in a multitechnology simulator. Results show that the proposed solution outperforms mainstream approaches. [ABSTRACT FROM PUBLISHER]