Yiling Xu

2-D Index Map Coding for HEVC Screen Content Compression

This paper introduces a 2-D index map coding of the palette mode in screen content coding extension of the High-Efficiency Video Coding (HEVC SCC) standard to further improve the compression performance. In contrast to the current 1-D search using RUN to represent the length of matched string, we bring the block width and height to describe the arbitrary rectangle shape. We also use the block vector displacement to signal the matched block distance efficiently. By enlarging the search range from current coding tree unit (CTU) to a small neighbor CTU window (i.e., 3×5 CTUs), it provides the coding efficiency comparable to the case that full-frame intra block copy is used. It is more practical to use the local search window in real life considering the trade-off between the coding efficiency and implementation cost.

An MMT based heterogeneous multimedia system using QUIC

MPEG has developed a new multimedia transport protocol namely MMT for heterogeneous network transport. Considering the characteristics of both broadcast network and internet, MMT has many advantages compared to traditional transport protocols like MPEG2-TS, RTP and so on. Although MMT has defined a detailed transport protocol for broadcast delivery, the broadband transport protocol of MMT has not been implemented specifically. Nowadays, HTTP is the first choice in most MMT systems. In this paper, we develop a new MMT system using QUIC which is developed by Google as a transport protocol atop UDP as the broadband protocol. This system combines the advantages of MMT and QUIC, thus can achieve better performance than existing MMT systems. We also conduct experiments to compare the performance of the two systems. The experiment results verify that using QUIC in MMT can improve the broadband media transport in MMT system.

Lossless point cloud geometry compression via binary tree partition and intra prediction

Characterized by geometry and photometry attributes, point cloud has become widely applied in the real-time presentation of various 3D objects and scenes. The development of even more precise capture devices and the increasing requirements for vivid rendering inevitably induce huge point capacity, thus making the point cloud compression a demanding issue. Considering the non-uniform sampling and time-variant geometry, appropriate structural representation for point cloud is important. In this paper, we propose a lossless geometry compression algorithm for 3D point cloud which serves as the basis of future adaptive improvement. We utilize the binary tree structure for effectively partitioning unorganized points into block structure. This hierarchical representation obtains roughly the same quantity level for each leaf node. Further analysis is conducted on an intra-geometry prediction via extended Travelling Salesman Problem (TSP), achieving an impressive performance in eliminating point-wise redundancy while preserving one single reference position for each block. The residual encoding is accomplished via a shallow neural network-based lossless compression algorithm, PAQ. Simulation results confirm the lossless compression of geometry from high quality capture, achieving approximately 3.5 times efficiency gain over the state of art algorithm implemented as MPEG Point Cloud Compression (PCC) reference software.

DASH sub-representation with temporal QoE driven layering

For realizing finer granularity rate adaptation with tradeoff between visual quality and network bandwidth, DASH provides sub-representation structure that can further refine QoE options with multiple temporal quality operating point at a fixed spatial quality, i.e., PSNR. In order to facilitate streaming QoE driven decisions with sub-representations, in this paper, we shall develop a new structure for constructing DASH sub-representations. Besides, we utilize the fame significance metric to characterize the temporal quality and introduce a gradient driven temporal quality layer packing algorithm based on the frames loss induced distortion, which can be used as one theoretical foundation of constructing DASH sub-representations for matching the available throughput with the improved spatio-temporal distortion tradeoff.

View-Dependent Streaming of Dynamic Point Cloud over Hybrid Networks.

Characterized by efficient and exquisite representation of the objects or scenarios in the real world, 3D point cloud has been widely applied in large amount of emerging applications such as virtual reality/augmented reality, automatic drive, gaming technologies or robotics. Each point of the data contains 3D geometry information and corresponding photometry information like color, intensity, normal or texture, leading to massive data capacity and severely influence the transmission quality with limited network resources. However, more than a half of the points in each point cloud frame are invisible as being occluded by others from the main viewpoint. To deal with the above issues, we propose a view-dependent streaming for dynamic point cloud based on the novel hybrid networks. We project the point cloud frame into six 2D frames and generate videos with different bitrates in consideration of various user interests. Therefore, differential transmission can be achieved such that the personalized contents like the current consumed viewpoint are transmitted via interactive broadband channel, while the less-attention contents can be pushed through general digital broadcasting channel. Therefore, benefit from existing hybrid transmission systems, reliable services with efficient utilization of limited transmission resources are achieved. Experimental results have shown considerable bandwidth saving based on the proposed scheme, maintaining satisfying reconstruction performance.

Optimal DASH-Multicasting Over LTE

Dynamic Adaptive Streaming over HTTP (DASH) is a fast growing video streaming platform, which enables adaptive rate selection based on channel conditions. File Delivery over Unidirectional Transport (FLUTE) further enables multicasting of the DASH segments over LTE eMBMS systems. In this paper, an optimal DASH-multicasting solution is proposed to allow more DASH clients in an LTE network to receive better videos by optimizing the resource allocation, application-layer forward error correction (FEC) code rate and modulation and coding scheme (MCS) selection of each multicasting group, which corresponds to a FLUTE session. Multiple FLUTE sessions are considered to deliver multiple videos, each with different video rates, for enhancing the overall utility. We have applied the convex optimization method to find the optimal resource allocation in terms of utility for multiple FLUTE sessions. We also find the optimal FEC code rates to add redundancies to protect the video segments for each FLUTE session. Moreover, an efficient MCS selection is introduced to reduce the complexity of the algorithm. Simulation results, with realistic LTE parameters, are shown to prove the proposed scheme is optimal, with more DASH clients receiving better video representations within limited resources when compared to other existing algorithms.

A Practical System Towards the Secure, Robust and Pervasive Mobile Workstyle

We develop an innovative PC2PC (personal computer to pervasive computing) system to enable the secure, robust and pervasive mobile workstyle. PC2PC server compresses the desktop screens of any virtualized system, and delivers the stream through any popular networks to PC2PC client remotely for stream decoding, rendering and end-user interaction (such as keyboard/mouse commands). We have implemented the overall system from the scratch, where the emerging screen content coding (SCC) extension of the High-Efficiency Video Coding (HEVC) is implemented to compress and stream the desktop screens in real-time, and three core asset channels (i.e., system, display, inputs, etc) are defined to enable systematic end-to-end communication. Compared with the commercial Red Hat SPICE virtual desktop infrastructure (VDI) scheme, our PC2PC could save the network bandwidth by a factor of 2, 7 and 4 respectively for typical video streaming, web browsing and stationary office applications at same visual quality. Meanwhile, we have also measured the delays in the system and presented the preliminary study on the user experience impact. A simple network estimation is applied to optimize the quality-bandwidth adaptation for both single user and multiuser、 scenarios to combat the network dynamics.

Compact scalable hash from deep learning features aggregation for content de-duplication

Unprecedented growth in media content generation, communication and consumption has taken over the vast majority of storage spaces in devices, network caches, and clouds. How to identify duplications from network caches is an important issue for fast and efficient content delivery network (CDN) communication and storage. In this work, we developed a novel hash scheme which is scalable and robust to typical CDN induced transcoding and manipulations. Scalable hash design is constructed in essentially two stages: images are first represented as 512 channels of thumbnail images from the deep learning VGG-16 networks, and then a Fisher Vector aggregation is performed on the features which offer scalability in both underlying Gaussian Mixture Model (GMM) PCA embedding and component posterior likelihood. Hash is generated by direct binarizing the Fisher Vector with component/dimensionality priority optimization. Simulation results have demonstrated that this is a very compact and accurate scheme for CDN content de-duplication.

QoE based cache control in wireless multicast

In this paper, we investigate the problem of optimal content cache strategy with content of multi-available streaming rates over wireless network of different user channel condition. Our design objective is to maximize the quality of experience (QoE) at the user end under a limited cache server storage when considering the different channel condition among users. More specifically, the proposed strategy chooses the optimal subset of streaming rates based on channel condition and storage budget so that the expected QoE value at the user end is maximized. As a result, the propose strategy promote the utilization efficiency of insufficiency network resources and cache budget. Furthermore, through the analysis of the simulation results, there is a tradeoff between the cache size and the QoE value, depending on the number of users and their channel condition. The proposed cache strategy is based on the MMT standard.

Performance Enhancement of NAND Flash Using Unequal Error Protection

NAND flash memories are not error-free. The Program/Erase cycles and retention time are two major factors affecting the reliability of NAND flash memories. Most error control codes (ECC) used in a flash memories provide a uniform protection regardless of the different raw bit error rate (RBER) of different storage cells, which fails to take full advantage of the limited available redundancy. To optimize the redundancy and take the unequal RBER into account, an unequal error protection scheme is proposed in this paper to improve the performance of NAND flash. Simulation shows that UEP method is more flexible and performs better in most condition.