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Inter-destination media synchronization (IDMS) is a key requirement to enable successful networked shared media experiences between remote users. This paper presents an adaptive, accurate and standard-compliant IDMS solution for hybrid broadcast and broadband delivery. Apart from providing multi- and cross-technology support, the presented IDMS solution is able to accomplish synchronization when different formats/versions of the same, or even related, contents are being played out in a shared session. It is also able to independently manage the playout processes of different groups of users. The IDMS solution has been integrated within an end-to-end platform, which is compatible with the hybrid broadcast broadband TV standard. It has been applied to digital video broadcasting—terrestrial technology and tested for a Social TV scenario, by also including an ad-hoc chat tool as an interaction channel. The results of the conducted (objective and subjective) evaluations prove the statisfactory behavior and performance of the IDMS solution and platform as well as in terms of the perceived quality of experience.

High throughput satellites employing multibeam antennas and full frequency reuse for broadband satellite services are considered in this paper. Such architectures offer, for example, a cost-effective solution to optimize data delivery and extend the coverage areas in future 5G networks. We propose the application of the multiple-input-multiple-output (MIMO) technology in both the feeder link and the multiuser downlink. Spatial multiplexing of different data streams is performed in a common feeder beam. In the user links, MIMO with multiple beams is exploited to simultaneously serve different users in the same frequency channel. Under particular design constraints, effective spatial separation of the multiple user signals is possible. To mitigate the inter-stream interference in the MIMO feeder link as well as the multiuser downlink, precoding of the transmit signals is applied. Simulation results illustrate the performance gains in terms of sum throughput.

By jointly using layered division multiplexing (LDM) and scalable hierarchical video coding (SHVC), ATSC 3.0 will be used to deliver service targeted to both mobile and traditional rooftop antenna receivers. Under this approach, a physical layer pipe (PLP) with robust coded modulation will ensure mobile reception in an area comparable to an ATSC 1.0 TV station’s current 15 dB rooftop coverage. However, the same approach will also give rise to increased coverage overlap between TV stations, as the same robust PLP will be decoded by rooftop receivers over a much larger area beyond ATSC 1.0’s 15 dB contours. When multiple independently owned TV stations affiliated to the same broadcast programming network overlap the subscriber footprint of a multichannel video program distributor (MVPD) they risk becoming redundant affiliates so the MVPD could pit them against each other in bargaining over lower retransmission consent fees. We calculate the potential increase in rooftop coverage overlap among same-network affiliates of the four largest U.S. over-the-air broadcast programming networks in the U.S. (ABC, CBS, NBC, and Fox). We find that as much as 75% of the population will have access to at least one redundant network affiliate, and 60% will have access to two affiliates or more. Under U.S. federal communications commission (FCC) rules, MVPDs can only pit affiliates against each other if the out-of-market TV station is classified by the FCC as significantly viewed (SV) on a per-county basis. Thus, we analyze if an increase in coverage overlap would in fact increase the number of counties where out-of-market stations become SV. We build a predictive random forests model based on current ATSC 1.0 15 dB coverage and the publicly available list of existing SV stations. With an LDM upper layer with a reception threshold of 2 dB —equivalent in bitrate to one full-HD 1080p program—, the model predicts that the number of county/SV station pairs would increase by 100% or more. This suggest that despite all the benefits of the LDM + SHVC approach, and the potential to increase revenues through service to mobile consumers, there is a risk that this approach may negatively affect TV broadcasters’ retransmission consent revenues. We discuss a number of potential trade-offs and solutions that can help to mitigate this issue.

This paper introduces a self-interference cancellation (SIC) technique for data over cable service interface specification (DOCSIS) 3.1 uplink system with full duplex. At DOCSIS 3.1 uplink environment with full duplex, cable modems (CMs) transmit their request information (upstream) to CM termination system (CMTS), and CMTS transmits its broadcasting data (downstream) through the same frequency band at the same time. Therefore, the upstream becomes a signal of interest (SoI), and consequently the high-power feedback downstream causes self-interference (SI) signal against SoI. However, under the practical DOCSIS 3.1 uplink environment, achieving time and frequency synchronization between upstream and downstream is a challenging problem and additional signal processing technique should be applied. Therefore, in order to improve the processing efficiency and to reflect practical communication environment, the time-domain SIC technique, which does not require any synchronization between upstream and downstream is proposed. Furthermore, since the proposed scheme can consider various practical signal processing aspects, such as multi-path fading channel estimation and nonlinear element estimation of SI signal, it can be efficiently applied for the practical DOCSIS 3.1 uplink system. Test results indicate that the uplink system with 1024QAM (mandatory maximum constellation) can be operated in the case that there is SI estimation interval with two OFDM symbols.

Appropriate coverage prediction is a fundamental task for an operator during the dimensioning process and planning of a digital terrestrial television (DTT) system because it allows offering a satisfactory quality of service to end users. Accordingly, several prediction methods based on propagation path loss estimation and traditional statistical models have been proposed. However, the choice of model depends on many factors, such as the presence of obstacles (buildings, trees, and so on) and propagation paths. This fact leads to increasing the error gap between the predicted and real value, which varies from one propagation model to the next. Therefore, novel techniques are required to achieve a high accuracy in the prediction of the signal strength based on few local measurements over the zone of interest. A machine learning regression algorithm is a novel approach that improves the accuracy of DTT coverage prediction regardless of the aforementioned constraints. To this end, we propose an approach based on clustering and machine regression algorithms, such as random forest regression, AdaBoost regression, and ${K}$ -nearest neighbors regression, where we choose the best algorithm for our approach. We use real measurements in terms of electric field strength corresponding to eight DTT channels operating in the city of Quito, Ecuador. Furthermore, we display the coverage results in Google Maps. We perform extensively simulation analysis based on the tenfold cross validation method to evaluate the performance of the machine learning regressor algorithms and compare the results in three error metrics with support vector regression, lasso regression, multilayer perceptron regression, and ordinary kriging technique. Satisfactorily, the results using random forest regression depict a considerable improvement in the accuracy of coverage prediction under a low computational load.

Similar to orthogonal frequency division multiplexing receiver, the frequency-domain (FD) channel estimation (CE) and equalization are indispensable parts in the coherent single carrier frequency division multiplexing (SC-FDM) receiver. When the channel varies slowly, the FD processing is cost effective. For the applications with high mobility, the traditional implementation of the SC-FDM receiver causes significant performance degradation. In this paper, we propose to estimate time-varying pieces of channel taps for pilot symbols based on basis expansion model (BEM), and subsequently to reconstruct time-domain (TD) channel response for data symbols by utilizing the Slepian sequences-based piece-wise interpolation. Furthermore, two simplified schemes, i.e., the Slepian sequences-based multiple-point interpolation and the segmented BEM, are developed to reduce the computational complexity of the TD-CE. The Cramer-Rao lower bound (CRLB) of channel impulse response (CIR) estimation is also analyzed. In the light of the sparsity of TD channel gain matrix, we design an iterative least-squares QR decomposition algorithm to equalize the SC-FDM symbols. In the simulated SC-FDM system, when considering the carrier frequency of 5.9 GHz and the velocity of about 510 km/h, we observe that the traditional FD methods cause the demodulation failure, while the proposed TD processing schemes achieve ideal error-probability performance and preserve a relatively low complexity.

The current version of the data over cable service interface specification (DOCSIS), which is DOCSIS 3.1, is markedly different from previous versions in that orthogonal frequency-division multiplexing (OFDM) is used together with high-density QAM constellations (up to 4096-QAM) in both the downstream and upstream directions to facilitate high data rates. This is in a sharp contrast with the single-carrier modulation techniques used in prior versions of DOCSIS. Despite many advantages, a drawback of OFDM is the insertion of a cyclic prefix (CP) at the beginning of each transmitted OFDM symbol to mitigate inter-symbol interference (ISI). The CP, which is configurable, but can be as large as 25% of the symbol time for DOCSIS 3.1 downstream transmission, does not carry useful data and therefore reduces the overall transmission throughput. In general, not only the channel but also the transceivers’ digital filters determine the ISI and therefore the length of the CP. In this paper, the aspects that determine the CP length are thoroughly addressed. A novel technique to eliminate the CP is introduced and analyzed. It is demonstrated that the CP can be completely removed with a negligible effect on the transmission performance using this technique. Moreover, the proposed technique requires only a modest amount of hardware, making it a promising candidate to be included in the a future version of DOCSIS.

There is an increasing user demand for high-quality content-rich multimedia services. Despite their advantages, current wireless networks in general and wireless mesh networks in particular have limitations in terms of quality of service (QoS) provisioning, especially when dealing with increased amounts of time sensitive traffic such as video. This paper presents video load balancing solution (ViLBaS), a load balancing-based mechanism which enhances delivery performance of video services over multi-hop wireless mesh networks and improves user quality of experience (QoE) levels. ViLBaS involves performance monitoring at the level of the nodes and load balancing by off-loading traffic from loaded nodes to less loaded neighboring nodes. Simulation-based results show how the proposed ViLBaS improves video delivery performance in terms of both QoS and QoE metrics [delay, throughput, packet loss, and peak signal-to-noise-ratio (PSNR)]. The comparison is performed against three other traditional approaches in different network topologies, for diverse video flow distributions, and different sizes for the video queue.

The bootstrap in ATSC 3.0 is expected to act as a universal wake-up signal for various wireless systems in addition to broadcast network. However, the signaling decoding performance of the standardized bootstrap degrades significantly in channels of fast time-variation and strong multipath. Furthermore, part of available bandwidth is reserved to ensure the compatibility with mobile communication network (MCN), which puts a limitation on the performance improvement. In this paper, to make the best of the available bandwidth, we introduce the bandwidth-concerned version information to enable an adaptive bandwidth configuration for the proposed bootstrap. Moreover, a 2-D signaling scheme is used to increase signaling capacity by selecting different gold sequences in frequency domain (FD) and simultaneously applying cyclic shift in time domain (TD). At the receiver side, we first provide improved estimators of symbol timing offset (STO) and fine frequency offset (FFO) for the bootstrap with special TD structure. Meanwhile, a learning-based binary classifier taking the output of STO estimator as training data is provided to generate an SNR-independent threshold for spectrum sensing without requiring the knowledge of channel conditions nor noise estimator. Afterwards, an inverse fast Fourier transform (IFFT)-based algorithm is used to decode FD signaling in the presence of unknown TD signaling, which allows 3-bit higher signaling capacity. Numerical analysis and simulation results demonstrate that the proposed adaptive bootstrap design as well as corresponding receiver algorithms significantly outperform the standardized one in terms of synchronization, detection and signaling decoding.

Recently, a new structure of low density parity check (LDPC) code named raptor-like LDPC code has attracted much attention. It has better performance at low code rate. In this paper, a novel decoding scheme for raptor-like LDPC code is proposed. First, the Gaussian approximation density evolution (GADE) algorithm is used to track and analyze the message transmission during the decoding process. It is found that certain log likelihood ratio (LLR) messages can be approximated by “zero” setting in the early iteration of raptor-like LDPC decoding. In other words, some column and row operations could be eliminated without compromise the performance. Next, we propose a new decoding scheme, which can skip unnecessary column and row operations. In comparison with the traditional belief propagation (BP)-based LDPC decoding scheme, the proposed scheme can reduce the decoding complexity. Additionally, a new algorithm is developed to facilitate the selection of the early iteration number. With this novel design, the proposed decoding scheme performs almost the same as the traditional BP-based scheme. To proof the concept, the raptor-like LDPC codes in the ATSC3.0 digital TV system are used to evaluate the proposed scheme, in comparison with the traditional BP-based schemes, i.e., sum-product algorithm (SPA), offset min-sum algorithm (OMSA), and normalized min-sum algorithm (NMSA). The simulation results confirm that the proposed scheme can reduce the decoding complexity without sacrifice in performance for all SPA, OMSA, and NMSA methods. About 10% complexity reduction can be achieved. This will reduce the buttery consumption for Internet of Things (IoT) and handheld devices.

The latest video coding standard H.265/HEVC is developed to succeed the previous coding standard H.264/AVC. However, a large amount of legacy content was coded with H.246/AVC. Therefore, transcoding from H.264/AVC to H.265/HEVC format is required. During the process of transcoding, we can easily calculate the distortion of the transcoded video with respect to the H.264/AVC-decoded video. However, since the original video is usually unavailable, the distortion between the original video and the transcoded video is unknown, which makes it difficult to control the coding quality of the transcoded video compared to the original video. In this paper, we propose a novel and accurate quality estimation method for transcoded videos utilizing probability theory. Experimental results demonstrate that the predicted quality of transcoded videos approximate the true value, with an average error of 0.28 dB, 0.41 dB, and 0.46 dB for Y, Cb, and Cr components, respectively.

2019 Scott Helt Memorial Award was awarded to Angel Martin, Jon Egaña, Julián Flórez, Igor G. Olaizola, Marco Quartulli, Jon Montalban, Roberto Viola, and Mikel Zorrilla for their paper, “Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks.” The paper appeared in the IEEE Transactions on Broadcasting, vol. 64, no. 2, part. 2, pp. 561–574, June 2018. The purpose of the IEEE Scott Helt Memorial Award is to recognize exceptional publications in the field and to stimulate interest in and encourage contributions to the fields of interest of the Society.

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