ABSTRACT. In the first phase of the terrestrial UHD broadcasting in Korea, we have focused on 4K production / transmission workflow and hybrid services. Now every detail of application standards is almost finished including emergency warning protocols, we are ready to jump into the next stage of UHD broadcasting. In this presentation, the current status of UHD TV in Korea and the new technologies considered in the next phase will be briefly introduced. 

ABSTRACT. Next Generation Audio brings a number of innovations to the broadcast industry. The key words are immersive audio and personalization. The underlying technologies and the user benefits will be highlighted and the first adaption of Next Generation Audio in broadcasting will be described. 

ABSTRACT. Our proposition is to construct a hybrid broadcast broadband network, based on cable, wireless, terrestrial and satellite networks. The converged network is supposed to provide nation-wide broadcast coverage collaboratively and to provide broadcast broadband converged services to end users.

ABSTRACT. A novel Layered Division Multiplexing (LDM) With Index Modulation (LDM-IM) system is proposed in this paper. It employs the Index Modulation (OFDM-IM) technology to enhance the transmission performance of the original LDM system by transmitting extra bits through the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers indices. The proposed system is based on a two-layer, Upper Layer (UL) and Lower Layer (LL), LDM system that serves two independent data services for at least two User Equipment(s) (UE) simultaneously. Besides this, by exploiting the Index Modulation (IM), each UE can receive the extra bits by decoding the subcarriers activation patterns. To map the extra bits to the subcarriers, a simple random codebook is designed in the proposed system based on the concept of OFDM-IM. To proof the availability and reliability of the proposed system, two metrics are chosen to evaluate the system performance, the average Symbol Error Rate (SER) and the transmission rate. In this paper, the architecture of the proposed system is introduced firstly. After that, the average SER of it is analyzed and verified by the Monte Carlo simulation. Finally, the transmission rate of the proposed system and the original LDM system is compared and evaluated.

ABSTRACT. In BMSB’2018, the authors presented a paper of using Layered-Division-Multiplexing (LDM) for in-band backhaul of robust mobile service for ATSC 3.0 SFN [1]. Only robust mobile services are backhauled to form an SFN in indoor or isolated environments to extend and improve reception QoE for handheld devices. This paper proposes an enhanced full in-band backhaul approach which provides backhaul of both robust mobile and fixed high data rate services. The method uses LDM transmission to carry backhaul data alongside broadcast data intended for reception by the public. The system is fully compatible with the ATSC 3.0 Next Gen TV service, causing no degradation of consumer service reception. The scheme can greatly reduce broadcasters’ operating costs, while improving service quality – especially for mobile, handheld and indoor reception. In comparison to On-Channel-Repeater (OCR), the proposed system can provide SFN service with flexible emission timing control. The regenerated signals at each transmitter can have high SNR to meet RF spectrum mask requirement and support multi-hop operation. A multi-hop backhaul system can form a mash network for reliability (re-route, if there is transmitter failure), and scalability to follow the population and market growth. The system can provide backhaul for SFN broadcast services, it can also be used for non-broadcast data distribution, such as IoT and connected car.

ABSTRACT. - Layered Division Multiplexing (LDM) is an effective approach for increase of data rates available for different services in wireless channels. LDM is included in ATSC 3.0 digital terrestrial TV system based on orthogonal frequency division multiplexing (OFDM) modulation. At the same time, satellite DVB-S2 and DVB-S2X systems that are currently used for Direct-To-Home (DTH) broadcasting include Variable Coding and Modulation (VCM) mode, which actually provides the opportunity to deliver different services to the consumers with Time Division Multiplexing (TDM). From this point of view, the investigation of LDM for satellite broadcast and communication systems and its comparison with VCM (TDM) approach becomes relevant. In this paper, several use cases were considered providing the delivery of High Definition (HD) and Ultra High Definition (UHD) TV services to customers as well as additional communication services. Analysis shows that LDM provides considerable gain in comparison with VCM for each use case.

ABSTRACT. This paper presents the capacity analysis of 3-layer LDM system to proof that a 3-layer LDM has capacity advantage than that of a TDM/FDM system. The third layer SNR threshold is optimized based on total system capacity and the requirement of the third layer performance.

ABSTRACT. A compact ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna, with low mutual coupling, is proposed for UWB MIMO applications. Two coplanar annular monopole planar UWB antennas are placed side by side on the substrate with a dielectric constant of 4.4 and a size of 40 mm × 80 mm × 1.6 mm. In order to reduce the mutual coupling between the two elements, the expanded ground is exploited. The proposed antenna achieves a good impedance matching with S11 < -10 dB and a low mutual coupling with S12 < -20 dB over the entire UWB band.

ABSTRACT. With the development of broadcast video applications, the demand for broadcast video is increasing, which poses great challenges to the existing broadcast systems. The mmWave is capable of supporting large bandwidth. In addition, code domain NOMA is able to enhance the spectral efficiency. Therefore, combining the advantages of these two methods, the large-volume content services can be improved. This paper proposes to exploit the joint benefits of mmWave and code-domain NOMA to achieve further capacity gains in broadcast services. Theoretically analysis and numerical results validate the performance gain of the proposed scheme over conventional scheme with respect to spectral efficiency.

ABSTRACT. Considering the huge training cost and feedback overhead of wireless communication system where base station is equipped with large-scale antenna array, an energy-efficient mixed-timescale hybrid precoding is proposed in this paper. The design of hybrid precoding is decoupled, where analog precoder and digital precoder are determined by second-order statistical channel state information (CSI) and instantaneous CSI respectively. Moreover, for analog precoding, a fixed subconnected architecture is considered to further reduce the hardware complexity and power consumption. It transforms the optimization of analog precoding matrix into low-dimensional problems. Meanwhile, a two-step heuristic method is proposed to obtain near-optimal solution. Simulation results show that our proposed scheme has a good trade-off between performance and hardware complexity, power consumption for massive MIMO.

ABSTRACT. Millimeter wave (mmWave) with its large spectrum bandwidth is an attractive technology to achieve high date rate, high security and so on. However, it is always difficult to obtain channel state information (CSI) since the mmWave signals undergo large attenuation. This paper proposes a channel tracking algorithm for mmWave multiple input multiple-output (MIMO) systems, where hybrid precoder and combiner are employed by the transmitter and the receiver, respectively. The algorithm uses extended Kalman filter (EKF) to track the CSI and updates the hybrid combiner to improve tracking accuracy further. Simulation results prove that the proposed algorithm has lower tracking error compared to prior work.

ABSTRACT. Deep convolutional neural networks have achieved remarkable performance on object detection tasks. Regression based models include YOLO and SSD are faster and more accurate, but they still run slowly on devices with limited computational and memory resources. Many work focus on network pruning so that they can be effectively deployed. However, most of them require expert knowledge and massive experiments to determine which layers to prune, and how many channels to prune in every single layer. In this paper, we firstly define the importance score of a channel as the product of its L1-norm of weights and scaling factor of batch normalization layer. Then we adopt Lasso regression and fine-grained regularization coefficient to learn sparse scaling factor. By pruning channels with small importance score then retraining, we can get a compact model without significant accuracy drop. Compared to other rule-based pruning method, our method achieves higher accuracy on Pascal VOC2007 dataset while requires less human effort on channel selection.

ABSTRACT. In this paper, we introduce audio channel extension tool using spatial audio cue predicted by convolutional neural networks. The channel extension tool is applied into the common stereo signals to produce high dimensional audio signals such as 5.1 layout. To extend channels from stereo signals, we predict spatial cues from stereo signal and multichannel signals are synthesized by allocating spectral components according to the direction of predicted spatial cues. Our subjective evaluation shows that synthesized multichannel signals guarantee good quality compared with those of original stereo signals.

ABSTRACT. Real-time interaction has become increasingly important. At the same time, panoramic video has gradually become popular. In this paper, the problem we study is predicting the Field-of-View(FoV) at the future moment when people are enjoying a dynamic panoramic immersive video. Existing methods either estimate the future viewing area based on the previous trajectory, or predict the FoV based on salient region in video frames. Here, we design a new model to predict the viewing points in future moments. Firstly, we predict a point from the viewer's previous viewing trajectory using LSTM(Long Short-Term Memory) network. At the mean time, panoramic video frames are mapped to 6 patches by cube map in advance. The modified VGG-16 network is used for each patch image to perform saliency detection. Then, these 6 salient maps are combined to a single salient map as output. A 3-layer convolutional neural network to refined the salient map is utilized. Finally, the salient map of corresponding moment is combined with the predicted point by LSTM input to a two-layer fully connected network to produce the final predicted point. The experiment results show that our model's prediction accuracy is higher than the traditional prediction algorithm and has better performance than the model without using the second CNN network.

ABSTRACT. Smoothlets, as one of the adaptive geometrical multi resolution methods, are efficient in the approximation of images with various edges. However, when multiple edges present in blocks, smoothlets become less efficient due to its requirement in further partitioning the blocks. To overcome this problem, multismoothlets have been developed, which could efficiently represent multiple edges with a vector of smoothlets. However, the improvement in terms of the quality of the reconstructed image by the existing implementations of serial and parallel multismoothlets is relatively small. In this paper, we propose a new residual multismoothlets method by iteratively calculating the smoothlets at the original image and multiple levels of residual images to obtain the multismoothlets. Experimental results demonstrate that the proposed method can effectively improve the quality of the reconstructed image in image approximation compared with the existing multismoothlets.

ABSTRACT. This paper presents laboratory test results from multipath and impulsive noise interference in the brazilian Digital TV system, ISDB-TB, and in the broadcast system ATSC 3.0, adopted by USA and South Korea. These tests were made in a Faraday’s Cage,environment immune to external electromagnetic waves.

ABSTRACT. Japan Broadcasting Corporation (NHK) has been conducting research and development on large-capacity transmission technologies for next-generation digital terrestrial television broadcasting (DTTB) system. In this work, a transmission system inherited Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) has been developed, which can provide both fixed reception service and mobile reception service. The transmission system was developed with the aim of providing 4K/8K ultra high definition TV (UHDTV) broadcasting for fixed reception and HDTV broadcasting for mobile reception in one channel (6 MHz bandwidth). This report describes an overview of the transmission system for a next-generation DTTB called the advanced ISDB-T and its basic transmission performance.

ABSTRACT. Research into the possibility of a next generation Japanese terrestrial broadcasting system is underway in Japan. As part of that effort, a system based on frequency division multiplexing (FDM) as used in ISDB-T has been created. Multiple layers can be transmitted at the same time using different frequency divisions known as segments, such as a mobile layer and a layer for fixed reception. In this paper we report on simulations of performance of the mobile layer of this system using typical reception algorithms in a TU6 channel. We show that while a different sampling frequency has been adopted for the possible next generation system, and even with the choice of 16384 FFT, mobile reception under highway conditions can be obtained while increasing the data rate significantly.

ABSTRACT. Japan Broadcasting Corporation (NHK) has conducted research and development on advanced terrestrial broadcasting technology to transmit UHDTV and other high capacity content. The transmission performance of the system being developed (hereafter referred to as Advanced ISDB-T) is better than that of ISDB-T (Integrated Services Digital Broadcasting-Terrestrial). We developed a prototype modulator and demodulator for Advanced ISDB-T, and confirmed its performance by laboratory experiments. We also evaluated the Advanced ISDB-T carrying out large-scale field trials those are similar to actual broadcast propagation environment.

ABSTRACT. The traditional multimedia contents only stimulate sight and hearing human senses. Therefore, the research efforts try to provide realistic media contents to the users to stimulate the other senses, realistic media contents are media with multiple sensorial effects, with the aim of increasing user’s sense of reality and emotions strength through the five senses representation. In home environment, to deliver the additional effects, customary devices (e.g., air conditioning, lights, smartphones, etc.), provided of opportune smart features, can be deployed. In smart home use cases, the Internet of Things (IoT) paradigm has been widely adopted to connect smart devices. This paper presents user perception of IoT-based multi-sensorial media delivery to TV users in home entertainment scenario, through subjective test measurement campaign. In particular we analyzed the influence of three sensory effects (i.e., airflow, lights and vibration) on user annoyance, emotions, sense of reality, and if users consider the sensory effects distracting, through presenting ten audiovisual sequences that contain different emotions like surprise, anger, fear, fun, and worry, which people commonly use to describe emotions in audiovisual sequences. The participants’ response employed to reveal the impact of sensory effects on user experience.

ABSTRACT. How to efficiently transmit graph-data to mobile devices is quite appealing nowadays, especially for autonomous vehicles and positioning systems. When a mobile receiver is set out for a journey, the objective is often to reconstruct topologically-complete subgraphs according to the arrival data in real time. Hence, we dedicate a novel coding scheme to segmenting and arranging the data efficiently from a huge mesh-grid graph. When the data-rate is restricted, through our proposed partitioning and scheduling schemes, mobile receivers can greatly relax the requirement for the number of transmission times to reconstruct topologically-complete subgraphs. To evaluate our proposed techniques, we derive the expected number of transmission times required to reconstruct a subgraph theoretically. Moreover, we define the $k$-hop completeness to measure the probability of reconstructing a topologically-complete subgraph by any scheme. Our proposed new method greatly outperforms the conventional scheme theoretically and by simulation. Our proposed method can enable the future journey-aware dynamic mapping system on vehicles without any need of pre-stored huge map database.

ABSTRACT. In the current context of the seamless integration of a variety of devices within the Smart Home concept, the home TV screen has gained again a pivotal role in everyday life, acting as a central hub for audio and video broadcasting services, home automation and other devices, including smartphones, tablets and IoT - enabled devices. In this context, a standard compliant and platform independent solution for the television device is preferred as it can ensure a dynamic environment in which various devices are be able to contribute to the overall user experience. This paper proposes an architecture which seamlessly integrates the consumer’s phone and a smart home environment with a HbbTV-enabled television set, using the HbbTV 2.0 Companions Screen and Multimedia Synchronization framework. A first architectural design and its hardware implementation have been developed and deployed as a proof of concept. An initial set of tests has been conducted in order to assess the benefits of the proposed system.

ABSTRACT. To meet the needs of implementing the Internet of Thing (IoT) applications in rural areas and developing countries, an IoT model based on Dedicated Return Channel (DRC) in ATSC 3.0 is proposed in this paper. The model provides a possibility for broadcast operators to independently conduct IoT services. The proposed model transmits signaling information of IoT devices through PLP-R, in order to avoid congestion due to insufficient PLP-R capacity, we propose a heuristic uplink and downlink joint DRC-IOT resource scheduling algorithm. Our algorithm takes the overhead of signaling information and QoS into account and develops a new search strategy. Simulations and results show that our algorithm reduces the delay of Guaranteed Bit Rate (GBR) service and increases the throughput of Non-Guaranteed Bit (Non-GBR) service compared with NS algorithm and MBUS algorithm.

ABSTRACT. The generation of hotspots and rapid change of location put forward higher requirements for the flexibility of 5G wireless network. How to accurately predict the future traffic load is a prominent problem. In this paper, we use Gaussian Random Field (GRF)-based to fit the distribution of spatial traffic density and then locate the hotspots for broadband multimedia services. At the same time, the Long Short-Term Memory (LSTM) model is trained to predict the next moment traffic value by using the actual traffic data collected from base stations. Finally, the model is used to predict the future traffic data of base stations and locate the hotspots. Through analysis and evaluation, numerical results show that the method can effectively locate hotspots.

ABSTRACT. Broadcast of live sports and events often requires the coverage of a wide area and portable transmission units for the mobile cameras. In this context, the mobile network aspires to be a professional tool companion for media production to boost mobility and alleviate costs, space and specialist maintenance of satellite equipment. Transmission of live high quality captured video and graphic design to a cloud or distant studio production infrastructure requires high uplink data rates. However, steady and reliable communications are challenging for the network in disperse, distant and sparse areas. This context may need bonding multiple cellular links to ensure a sufficient Quality of Service (QoS). Video uplink solutions at different network layers can shield from QoS degradation. Communications industry solution for IP bonding consists on having different Long-Term Evolution (LTE) network interfaces with several sim cards on the device which transmits the live stream, then having network redundancy. This paper provides an innovative method to dynamically balance the throughput for each concurrently employed network interface in real-time at the live video transmitter. The solution exploits live measurements obtained from the network layer (L3), such as network bandwidth, latency and jitter, which are periodically assessed along the video transmission, and application layer (L7) state, such as the encoding GOP schema, frame type and framerate, to split the video packets in the different network interfaces. The evaluation of the solution is made for a head-end implementation by sending live video streams and measuring the QoS at the production infrastructure. To conclude the benefits when the solution comes into play, results are compared to a scenario without bonding solutions and another one where balance rates are initially fixed.

ABSTRACT. This paper propose a reconfigured strategy of slices in flex-grid optical network based on representation learning. In the proposed method, slices can adjusted the resources according to the changing of loadings. Experiments show our strategy has better resource utilization.

ABSTRACT. Today, the Internet of Things (IoT) is, in a broad sense, a network of the interconnected objects able to communicate with remote data centers over different wireless technologies. Starting from simple, non-standardized and even wired solutions for data collection from sensor-like devices, the IoT technology has evolved into a large-scale wireless system supporting different data traffic and device categories. The future IoT landscape includes not only low-latency and low-payload machine-type communication (MTC), but it is also expected to support the growing demand of multimedia data traffic to be communicated in smart environments. ***The extended abstract continues***

ABSTRACT. Digital Audio Broadcasting (DAB) is a widely used terrestrial audio broadcast transmission system. DAB employs Orthogonal Frequency-Division Multiplexing which allows the operation in a Single Frequency Network (SFN). SFNs are beneficial for the distribution of services in large areas, but do not support local services, i.e. different services from different transmitters, without distortion. However, some audio service providers rely on local services. A Local Service Insertion technique for DAB SFNs that supports existing receivers was developed, tested and presented in a field trial in Braunschweig, Germany. The results showed some co-channel interference between local services for existing receivers that use conventional differential detection (CDD). In this paper, we investigate an iterative decision-directed differential detection technique with channel equalization to combat the remaining co-channel interference. An increase in robustness against co-channel interference of about 3 dB is found in comparison to CDD.

ABSTRACT. In this paper, an approach to optimization of the single frequency network (SFN) for Digital Television Terrestrial Multimedia Broadcasting (DTMB) is proposed, aiming to resolve the issue that the design and construction of SFN is complex and time-consuming. The approach can be used to balance the performance and cost of an overlay network, and adopt the genetic algorithm to jointly optimize the delays of all transmission stations within it, then eventually create multiple SFN solutions that are optional to address the requirements on specific covered area and population located in a given region, thus considerably improving the efficiencies of SFN design and construction. The results of our tests indicate that the outcomes of SFN coverage optimization were measurably improved with the genetic algorithm for delay optimization of the transmission stations.

ABSTRACT. The visible light communication (VLC) system works well for indoor scenarios if the indoor coverage performance of the existing digital television terrestrial broadcasting (DTTB) system is not satisfactory. In this paper, we propose a hybrid system called the optics-radio hybrid single frequency network (SFN) with wide and deep coverage for DTTB, realizing seamless connection between outdoor and indoor. The hybrid system maintains the consistency of the baseband signal as well as the existing SFN structure of DTTB, and the hybrid receiver with antenna and photodiode can receive radio or optical signal individually or simultaneously. Furthermore, other functions of DTTB like positioning can also be improved.

ABSTRACT. This paper presents effect of distributed multiple input single output technique (MISO), known as transmit diversity code filter set (TDCFS), in mobile environments. Distributed MISO technique has been adopted in the ATSC 3.0 standard to reduce interference caused by multiple single frequency network (SFN) transmitters. Since TDCFS is designed to artificially make mild multipath-delayed channel to receivers, it can protect receivers from harsh deep fading caused by multiple SFN transmitters.

ABSTRACT. We propose a novel model of joint channel equalization and decoding based on recurrent neural network (RNN) in order to recover information messages interfered by channel distortion. By returning the output of decoder to the input of equalizer, an iterative training process is achieved. With less than 2/3 of the parameters, the proposed model has more than 0.5 dB gain over the CNN+NND-Joint model (and three other models) under the same channel distortion condition.

ABSTRACT. FBMC/OQAM is regarded as an alternative modulation scheme for OFDM. The classical Channel Impulse Response (CIR) and Carrier Frequency Offset (CFO) estimation methods based on scattered pilots in OFDM system cannot be directly applied to FBMC/OQAM system, because of the intrinsic pure imaginary interference. Traditional methods in FBMC/OQAM use scattered pilots and auxiliary pilots to estimate CIR and CFO, which increase system overhead and transmitter complexity. In this paper, we present a novel CIR and CFO estimation method with scattered pseudo-pilots and iterative decision feedback structure. Simulation results show that the proposed method can estimate the CIR and CFO accurately in frequency selective channel and BER performance is better than the estimation method based on auxiliary pilot. Moreover, time-frequency resources that used for pilot in the proposed algorithm is only half of auxiliary pilot based method.

ABSTRACT. This paper presents the deep learning-based distortion element estimation technique of self-interference signal for DOCSIS 3.1 system with full duplex. In the DOCSIS 3.1 system with full duplex, the self-interference signal estimation and cancellation are the most important issues to enhance the system performance. Proposed technique estimates channel and nonlinear element of self-interference signal by using deep learning method. The practical challenge of application of deep learning technique to communication system is that it is difficult to find proper deep neural network structure for a specific purpose of the system. The proposed estimation technique uses simple deep neural network structure to estimate channel and nonlinear element of self-interference signal in time-domain. The evaluation results of the deep learning-based proposed technique is better than that of existing algorithm-based estimation technique.

ABSTRACT. In this paper, a modified peak-to-average power ratio (PAPR) reduction scheme based on compressed sensing (CS) in OFDM is proposed. In previous papers, the clipping and filtering (CF) approach was used to reduce the PAPR of the oversampled signal. In this paper, the peak cancellation signal (PCS) is analyzed as a series of parabolic pulse under oversampling constraints. The single pulse can actually clip several adjacent peaks at one time, which inspires us to modify the traditional CF approach in CS recovery scheme. It is known that the performance of the CS is highly related to the sparsity of the recovered signal. The proposed clipping method could dramatically improve the sparsity of the PCS when oversampling is considered and can also reduce the computational complexity to O(N) without FFT and IFFI in the process. Meanwhile, a corresponding modified PCS recovery method is proposed based on CS theory to recover the sparse PCS before Out-of-Band (OOB) filtering. It shows better BER performance compared with traditional CS recovery method which tries to recover the PCS after OOB filtering.

ABSTRACT. Network dynamics, such as bandwidth fluctuation and unexpected latency, hurt users' quality of experience (QoE) greatly for media services over the Internet. In this work, we propose a neural adaptive transport (NAT) framework to tackle the network dynamics for Internet-scale interactive media services. The entire NAT system has three major components: a learning based cloud overlay routing (COR) scheme for the best delivery path to bypass the network bottlenecks while offering the minimal end-to-end latency simultaneously; a residual neural network based collaborative video processing (CVP) system to trade the computational capability at client-end for QoE improvement via learned resolution scaling; and a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) strategy to select the appropriate video bitrate for maximal QoE. We have demonstrated that COR could improve the user satisfaction from 5% to 43%, CVP could reduce the bandwidth consumption more than 30% at the same quality, and DRL-based ARS can maintain the smooth streaming with >50% QoE improvement, respectively.

ABSTRACT. 5G infrastructures are in the roadmap of content delivery services, aiming to forward all Broadcast and Broadband video traffic using a common telecommunication network architecture. Streaming services will benefit from 5G networks which promise higher capacity, higher bandwidth and lower latency than current infrastructures. However, the widely employed streaming technologies, such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH), require an intrinsic high latency of tens of seconds to enforce the Quality of Experience (QoE). These conditions turn MPEG-DASH unfavourable when compared with a traditional broadcast pipeline for live events in terms of latency. Therefore, improvements on latency of streaming technologies are necessary to deliver live broadcast services over 5G networks. The media industry proposed a Chunked Common Media Application Format (Chunked CMAF) in order to achieve latency under a second. In this paper, we show an implementation of a Chunked CMAF for MPEG-DASH live videos in a real deployment. To further evaluate the benefits of CMAF we evaluate the QoE results when delivering a legacy MPEG-DASH live content compared to a Chunked CMAF-powered one.

ABSTRACT. Virtual reality can provide users with immersive ex-perience. However, because of its high requirement of bandwidth, it is still challenge to transmit 360-degree video. To overcome this issue, tile-based VR transmission strategy was proposed to reduce the data by dividing the 360-degree video into several tiles and streaming corresponding tiles according to user’s viewport. While the prediction accuracy is only guaranteed in a short time and it may cause frequent rebuffering when the network fluctuates, two-tier buffer system was proposed to tackle this problem. Nevertheless the two-tier method doesn’t consider the switching frequency of rate in user’s viewport. In this paper, we propose a novel hierarchical update buffer management method by jointly considering the probability of rebuffering and the switching frequency of rate in FoV to improve user’s QoE. The simulation results show that our proposed algorithm outperforms other methods in proving the smoothness of the viewing experience.

ABSTRACT. Recent development of adaptive video streaming applications has contributed to the increase of already overloaded Internet traffic. These advancements have brought to public attention the need to invest in the development of new communication methods which can help control the network traffic. As the increase in network traffic is expected to continue it becomes obvious that the existent IP-based network infrastructure struggles to meet the traffic demand. To address this issue a novel internet architecture, Content Centric Network (CCN), has gained attention in the recent years. CCN aims to replace the host-to-host communication structure with a named based content delivery approach. As such, there is a need to review video delivery over CCN in order to asses its potential to satisfy users expectations and demand. Previous works assessing video streaming adaptation in CCN have based their adaptation mechanism on network conditions estimation. This paper proposes an innovative Buffer-Aware Dynamic Adaptive Streaming solution over CCN (BA-DASC). The solution leverage's the playback-buffer capacity to determine the video quality rate which will ensure highest user satisfaction is achieved.