Laura Toni

Multicriteria Optimization of Access Selection to Improve the Quality of Experience in Heterogeneous Wireless Access Networks

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.

Channel Coding for Progressive Images in a 2-D Time-Frequency OFDM Block With Channel Estimation Errors

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 2D 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 2D 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.

Does Fast Adaptive Modulation Always Outperform Slow Adaptive Modulation

Link adaptation techniques are important modern and future wireless communication systems to cope with quality of service fluctuations in fading channels. These techniques require the knowledge of the channel state obtained with a portion of resources devoted to channel estimation instead of data and updated every coherence time of the process to be tracked. In this paper, we analyze fast and slow adaptive modulation systems with diversity and non-ideal channel estimation under energy constraints. The framework enables to address the following questions: (i) What is the impact of non-ideal channel estimation on fast and slow adaptive modulation systems? (ii) How to define a proper figure of merit which considers both resources dedicated to data and those to channel estimation? (iii) Does fast adaptive always outperform slow adaptive techniques? Our analysis shows that, despite the lower complexity and feedback rate, slow adaptive modulation (SAM) can achieve higher spectral efficiency than fast adaptive modulation (FAM) in the presence of energy constraint, diversity, and non-ideal channel estimation. In addition, SAM satisfies bit error outage requirements also in FAM-denied region.

Uplink Resource Management for Multiuser OFDM Video Transmission Systems: Analysis and Algorithm Design

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.

Online learning adaptation strategy for DASH clients

In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations.

Interactive free viewpoint video streaming using prioritized network coding

In free viewpoint applications, the images are captured by an array of cameras that acquire a scene of interest from different perspectives. Any intermediate viewpoint not included in the camera array can be virtually synthesized by the decoder, at a quality that depends on the distance between the virtual view and the camera views available at decoder. Hence, it is beneficial for any user to receive camera views that are close to each other for synthesis. This is however not always feasible in bandwidth-limited overlay networks, where every node may ask for different camera views. In this work, we propose an optimized delivery strategy for free viewpoint streaming over overlay networks. We introduce the concept of layered quality-of-experience (QoE), which describes the level of interactivity offered to clients. Based on these levels of QoE, camera views are organized into layered subsets. These subsets are then delivered to clients through a prioritized network coding streaming scheme, which accommodates for the network and clients heterogeneity and effectively exploit the resources of the overlay network. Simulation results show that, in a scenario with limited bandwidth or channel reliability, the proposed method outperforms baseline network coding approaches, where the different levels of QoE are not taken into account in the delivery strategy optimization.

Channel Coding Optimization Based on Slice Visibility for Transmission of Compressed Video over OFDM Channels

Optimization of multimedia transmissions over wireless channels should be aimed at maximizing the video quality perceived by the final user. For transmission of video sequences over an orthogonal frequency division multiplexing (OFDM) system in a slowly varying Rayleigh faded environment, we develop a cross-layer technique, based on a slice loss visibility (SLV) model used to evaluate the visual importance of each slice. In particular, taking into account the visibility scores available from the bitstream, depending on the scenario, we optimize the mapping of video slices within a 2-D time-frequency resource block and/or the channel code rates, in order to better protect more visually important slices. The proposed algorithm is investigated for several scenarios, with different levels of information about the channel available in the optimization process. Results demonstrate that, for different physical environments and different video sequences, the proposed algorithm outperforms baseline ones which do not take into account either the SLV or the CSI in the video transmission.

In-Network View Synthesis for Interactive Multiview Video Systems

In multiview applications, camera views can be used as reference views to synthesize additional virtual viewpoints, allowing users to freely navigate within a 3D scene. However, bandwidth constraints may restrict the number of reference views sent to clients, limiting the quality of the synthesized viewpoints. In this work, we study the problem of in-network reference view synthesis aimed at improving the navigation quality at the clients. We consider a distributed cloud network architecture, where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. We argue that, in case of limited bandwidth from the cloudlet to the users, re-sampling at the couldlet the viewpoints of the 3D scene (i.e., synthesizing novel virtual views in the cloudlets to be used as new references to the decoder) is beneficial compared to mere subsampling of the original set of camera views. We therefore cast a new reference view selection problem that seeks the subset of views minimizing the distortion over a view navigation window defined by the user under bandwidth constraints. We prove that the problem is NP-hard, and we propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem under general assumptions that cover most of the multiview scenarios in practice. Simulation results confirm the performance gain offered by virtual view synthesis in the network.

Correlation-Aware Packet Scheduling in Multi-Camera Networks

In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time.In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time.

A study on the SPIHT image coding technique for underwater acoustic communications

In this paper, we consider the transmission of progressively encoded images over underwater acoustic links, where transmitted symbols are protected by Forward Error Correction (FEC). The allocation of redundancy is performed according to both a Basic and a Multiple Description (MD)-like technique. The performance of this system is analyzed in terms of the resulting Peak Signal-to-Noise Ratio (PSNR) as image packets are transmitted over the measured realizations of acoustic channel impulse responses. These measurements were taken near the coast of Marthas Vineyard during October 2008, in different environmental conditions. In the results, we quantify the performance improvement of the multiple description (MD) technique compared to the Basic allocation, thus suggesting its suitability for the transmission of images in the underwater acoustic scenario.