The rate-distortion in conventional digital systems is replaced by cost distortion in pseudo-analog systems where the cost consists of power and bandwidth. In this paper, we formulate the cost-distortion optimization problem in terms of a power-bandwidth pair versus distortion to bring an insight to pseudo-analog transmission. Using a divide-and-conquer strategy, the 3-D optimization problem of a power-bandwidth pair versus distortion is decomposed into two subproblems: power distortion and bandwidth distortion optimization. To solve the integer nonlinear optimization problem, we propose two prediction models that transform the partial summation of variances and the square roots of variances into continuous functions. The proposed models are used to derive the closed-form solutions for both optimization subproblems, and a tradeoff between power and bandwidth is discussed. Accordingly, the resource control algorithm is designed to allocate the fewest resources required to obtain specific video quality; power can be traded for bandwidth and vice versa. Our experimental results show that the proposed optimization models achieve stable perceptual video quality comparable to that of Groups of Pictures and use resources more efficiently than do SoftCast.

Hybrid digital-analog (HDA) video transmission is a new cross-layer design, which can be widely used in Internet of Things. The key problem of HDA video transmission is to find the optimal resource allocation between the digital and analog part. This paper presents a new general resource allocation algorithm for superposition coding-based HDA system. On one hand, in order to achieve successful decoding in digital part, the bitrate is controlled by the quantization parameter (QP), and the channel coding rate and modulation order are determined by the signal to interference noise power ratio, in which analog part is considered as the interference. On the other hand, the overall video quality is directly determined by the mean square error of analog part, which depends jointly on the data variance of the analog part, the power allocated to the analog part and the channel noise power. We propose a prediction model to describe how the data variance of the analog part changes with the QP in the digital part. Based on the proposed model, the power allocation of two parts can be quantitatively connected to form an optimization problem. We prove the convexity of the resource allocation problem and the gradient descent method is utilized in system implementation. With extensive simulations, the proposed algorithm is validated, achieving 1.4-5.3 dB gain over the conventional digital system, and 6.2-7.4 dB gain over pseudo-analog system in peak signal-to-noise ratio.

This paper presents a network slice design for ultra high definition (UHD) video broadcast/multicast to achieve higher network efficiency and improved quality of experience (QoE). The proposed network slice design consists of a rateless source compression scheme and an analog-coded SoftCast scheme. The rateless Spinal code is adopted to compress the video source at content server and the compressed source is transmitted from content server across wireless core network to the base station. Ana prioriinformation-assisted Spinal decoder is designed to utilize the sparsity of bit planes for compression. In the analog-coded SoftCast scheme, we design a new chaotic function-based analog code with negligible power penalty for the generalized Gaussian-distributed source in SoftCast because the existing chaotic functions designed for uniformly distributed sources suffer from serious power penalty in SoftCast. We also design a maximuma posterioriprobability decoding algorithm for the proposed analog code in order to exploit the statistics of video source asa prioriinformation to improve the performance. The experimental results show that the proposed rateless code-based compression scheme achieves efficient compression and approaches the bound of binary erasure channel. In particular, the 1/2 analog-coded SoftCast has almost 2 dB gain over conventional SoftCast with two repetitions, and the 1/3 analog-coded SoftCast has almost 3 dB gain over conventional SoftCast with three repetitions. The system simulations for the broadcast system show higher network capacity and improved QoE in the proposed UHD slice, because the reconstructed video quality of each user is commensurate with its channel condition.

This research solves the performance degradation problem of uncoded video system when the multiplicative noise caused by channel estimation error is not negligible. Through extensive analysis, we find that transmitting a specific part of source data by multiple channel use can decrease the mean end-to-end distortion and stabilize the video quality. However, under limited power and bandwidth constraints, allocating multiple channel use to one part of data means dropping another part of data. How to allocate the channel use to highly diverse video coefficients is an NP-hard problem. For the sake of practical implementation, a greedy iterative algorithm is proposed to achieve the optimal trade off between distortion decrease by multiple channel use and distortion increase from data dropping. Extensive simulations validate our analysis result. The proposed algorithm achieve 1.99~8.63dB gain compared to existing schemes without considering the multiplicative noise.

In contemporary multiuser multiple-input multipleoutput systems, it is common for the reception devices to have a varying number of antennas. When multicast is performed, the number of concurrent spatial streams is limited by the device with the least number of antennas, which prevents more capable devices from getting higher rates. In this paper, we address the antenna heterogeneity in wireless video multicast by the innovative design of multiple similar description (MSD) video coding and multiplexed space-time block coding (M-STBC). MSD coding generates multiple descriptions of a video and features that any linear combinations of the descriptions are decodable. The descriptions comprising of real numbers are further processed by transform and power allocation steps for efficient transmission in a power-constrained system. M-STBC puts symbols in similar descriptions to corresponding space-time positions and ensures decodability under any antenna settings and channel conditions. As a result, we build up a scalable video multicast system, named AirScale, which allows receivers with a various number of antennas to decode from a single transmission, and the reconstructed video quality improves with the number of equipped antennas. Evaluations on Sora shows that, in a {1, 2, 3, 4} × 4 system, AirScale provides baseline quality for one-antenna receiver and a much higher quality for multiantenna receivers. The gain over SoftCast is up to 3.5, 3.9, and 4.1 dB for two-, three-, and four-antenna receivers, respectively.

This research proposes a novel data assisted image transmission scheme, which utilizes a large amount of correlated images stored in the cloud to improve the spectrum efficiency and visual quality. First, a two-layer Coset coding is proposed for the DCT coefficients transmission. The most significant bits (MSB) of the coefficients are generated by the first layer Coset and together with a few low frequency coefficients are transmitted through the most reliable channel coding and digital modulation. The middle bits generated by the second layer Coset are discarded by the sender and the residual bits are transmitted through amplitude modulation. Based on the MSB and the residual bits, an approximation of the original image is reconstructed. With this approximation, a lot of correlated images can be retrieved from the cloud, which are used to recover the discarded middle bits. The two layer Coset coding can significantly decrease the data energy so as to improve the transmission power efficiency. Hence, the end to end distortion of amplitude modulation can be reduced. Second, the image quality can be further improved by joint internal and external denoising with the retrieved images. Simulations show that the proposed scheme outperforms conventional digital schemes about 4 dB in peak signal to noise power ratio (PSNR) and achieves 2 dB gain over the state-of-the-art uncoded transmission. At low signal to noise power ratio (SNR), an additional 2-3 dB gain is achieved. The visual quality comparison also validates the objective image assessment result.

This research studies robust uncoded video transmission over wireless fast fading channel, where only statistical channel state information (CSI) is available at the transmitter. We observe that increasing channel diversity for high priority (HP) data is essential to improving the robustness of video transmission in fading channels. By utilizing the noise and loss resilient nature of video, we find it possible to design a more robust system by re-allocating the power and channel uses among HP and LP (low priority) data. With total power and channel use constraints, we derive an optimal resource allocation scheme under the squared error distortion criterion. In particular, we first propose a new power allocation algorithm at given channel allocation. Second, based on the proposed power allocation algorithm, we design a channel allocation algorithm to strike the tradeoff between the diversity increase of HP data and the information loss of LP data. Third, under known noise power distribution, we derive the optimal resource allocation for uncoded video multicast. Simulations show that the proposed system achieves 2dB and 5dB gain in average and outage PSNR over Softcast in video unicast, and around 1.4dB and 4dB gain in multicast.

This research addresses the problem of robust linear video transmission over the Rayleigh fading channel, where only statistical channel state information (CSI) is available to the sender. We observe that discarding low-priority (LP) data and saving the channel uses for high-priority (HP) data can significantly improve the quality of the received video. We formulate an optimization problem that aims to minimize the total squared error of a multi-variant Gaussian random vector under the given bandwidth and power resources. To tame the complexity of this NP-hard problem, we analyze two sub-problems, namely power allocation and bandwidth allocation, and propose an iterative algorithm to approximate the solution. Subsequently, we propose a one-pass two-step fast algorithm that further reduces both algorithmic and computational complexity. A linear video transmission system is implemented based on the proposed algorithm. Simulations show that our system significantly outperforms Soft-Cast, and the PSNR gain at 5th percentile of 1000 test runs is between 4.0 dB and 7.5 dB under varying noise levels.

This paper challenges the conventional wisdom that video redundancy should be removed as much as possible for efficient communications. We discover that, by keeping spatial redundancy at the sender and properly utilizing it at the receiver, we can build a more robust and even more efficient wireless video communication system than existing ones.

This paper presents a novel compressive coded modulation (CCM) which simultaneously achieves joint source-channel coding and seamless rate adaptation. The embedding of source compression into modulation brings significant throughput gain when the physical layer data contain non-negligible redundancy. The kernel of CCM is a new random projection (RP) code inspired by the compressive sensing (CS) theory. The RP code generates multilevel symbols from source binaries through weighted sum operations. Then, the generated RP symbols are mapped into a dense constellation for transmission. The receiver performs joint decoding based on received symbols. As the number of RP symbols can be adjusted in fine granularity, the rate adaptation becomes seamless. Two key design issues in the proposed CCM are addressed in this paper. First, we consider the RP code design for sources with different redundancies. Three principles are established and a concrete implementation is given. Second, we devise a linear-time decoding algorithm for the proposed RP code. In this belief propagation (BP) algorithm, we find that computing convolution in time domain is more efficient than that in frequency domain for binary variable nodes. Moreover, we invent a ZigZag deconvolution to further reduce the complexity. Analysis show that the proposed decoding algorithm is nearly 20 times faster than the state-of-the-art BP algorithm for CS called CS-BP. Emulations on traced data show that CCM achieves significant throughput gain, up to 33% and 70%, respectively, over the Hybrid ARQ with compression and BICM with compression, under practical time-varying wireless channels.

This paper presents a novel design of linear random space time coding (LRSTC) scheme. This scheme is able to achieve the dual benefits for MIMO receiver rate adaptation: (1) rateless coding and modulation and (2) approximately universal tradeoff between diversity gain and multiplexing gain. In this scheme, an LRSTC symbol is the superposition of a block of randomly rotated QPSK symbols. Such superposition generates a dense constellation that ensures a high saturation rate. The random rotation guarantees the pseudo-orthogonality between QPSK symbols. Through utilizing the orthogonality of symbols, a power gain can be generated at receiver end. The power gain can be increased by transmitting more LRSTC symbols to the receiver in order to resist stronger noise of the MIMO system. Therefore, the rate can be seamlessly adapted through varying the number of transmitted symbols rather than the adopting conventional strategies in terms of modulation, coding and antenna selection. Moreover, the transmission of LRSTC symbols will span over all of the transmit antennas which allows the spatial diversity to be fully exploited. When the rank of MIMO matrix is greater than 1, more linear independent symbols can be received in each time slot. Hence, multiplexing gain can also be achieved. We have carried out both analysis and simulations to verify this LRSTC design. The simulation results show that LRSTC not only achieves the throughput comparable with the ideal conventional rate adaptation, but also accomplishes a seamless switching between diversity and multiplexing gain.

This paper aims at designing a Seamless Rate Adaptation for wireless networking which achieves smooth rate adjustment in a broad dynamic range of channel conditions. Conventional rate adaptation can only achieve a stair-case rate adjustment. Even when combining with hybrid ARQ, it suffers from an irreconcilable conflict between throughput and dynamic range. We tackle this problem from a new perspective by relying on modulation, instead of channel coding, for rate adaptation. We propose rate compatible modulation (RCM), in which modulation signals are incrementally generated from information bits through weighted mapping. Rate adaptation is achieved through varying the number of modulated signals. As more signals are transmitted, information bits gradually accumulate energy. The weights in bit-to-symbol mapping are delicately designed to ensure fine-grained energy accumulation so that smoothness and efficiency can both be achieved. We design and implement a rate adaptation system, called SRA and evaluate its performance through a software radio testbed. Results show that, under highly dynamic channel conditions, SRA achieves over 80% throughput gain over 802.11a adaptive modulation and coding, and achieves 28.8% and 43.8% gain over HARQ systems implemented with Turbo code and Raptor code. We believe that the concept of rate compatible modulation opens up a fresh research avenue toward the wireless rate adaptation problem.

The major challenge in wireless multicast is the heterogeneous channel conditions of multiple users. In video multicast, the combination of a layered video coding scheme and a layered transmission scheme can gracefully accommodate user heterogeneity. This paper presents MixCast, a novel physical layer scheme for layered transmission. The key innovation in MixCast is the rateless Euclidean symbol mapping which mixes base layer and enhancement layer bits into arbitrary number of wireless symbols using arithmetic weighted sum operation. This design brings two benefits when compared with the state-of-the- art physical layer technique known as hierarchical modulation (HM). First, MixCast uses a fixed modulation constellation, avoiding the complexity in adaptive modulation and coding when channel condition varies. Second, the rateless symbol mapping allows MixCast to achieve much smoother rates than the stair-shaped rates in HM. We implemented MixCast for typical video multicast over OFDM physical layer, and evaluate its performance against HM through both simulations and software radio testbed. In simulations, MixCast shows consistent gain of 3dB to 5dB over HM under various rate combinations. In the testbed experiments, MixCast achieves significant gain up to 15dB in video PSNR over HM for football sequence.

This research considers network coded broadcast system with multi-rate transmission and dual queue stability constraints. Existing network coded broadcast systems consider single rate transmission without receiver queue constraints. First, we shall illustrate that broadcast without network coding cannot support maximum throughput in wireless fading channels. However, the network coded broadcast poses new constraints for the receivers to manage stable queues while the fading channel characteristics suggest the broadcast to operate at multi-rate to achieve higher throughput. In this research, we propose a joint scheduling and network coding (JSNC) strategy for such network coded broadcast system to achieve maximum throughput under queue stability constraint. In a single cell broadcast networks with exogenous arrivals of packets at the base station, we prove that JSNC can stabilize the system as long as the rate of the exogenous arrival flow is within the capacity region. Sufficient control parameters are provided in JSNC for trading off between sender's buffer and the receivers' buffers. JSNC can be viewed as a generalization of the classical backpressure scheduling rule to coded information flow. Alternatively, JSNC can also be viewed as an extension of network coding theory to queuing system.