Chun-Yuan Chang

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For low-delay video transmission, we introduce a q -domain characteristic-based bit-rate model. Specifically, three characteristics are efficiently extracted from the quantized DCT spectra to construct the bit-rate model. Extensive experimental results show that our rate model can provide more accuracy with lower complexity than existing models.

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Modern mobile devices have become an important part of our daily life but the performance of multimedia applications still suffers from the constrained energy supply and communication bandwidth of the mobile devices. In this work, we develop an energy-efficient streaming system for mobile hotspots to achieve better Quality-of-Experience. Our main idea is (a) to avoid redundant 3G transmissions as well as reduce the usage of 3G links for those low residual-energy users, and (b) to enable nearby mobile users cooperatively to share the downloaded data via short-range interfaces. The experiment results shows our scheme can improve the system lifetime by 27%, and provide better throughput as well as lower loss rate than conversional 3G systems do.

-Domain Characteristic-Based Bit-Rate Model for Video Transmission

In this paper, we present a two-layer rate control framework for low delay video transmission based on a characteristic-based rate-quantization (R-Q) model. Specifically, with the frame-layer characteristic-based R-Q model, we are able to figure out the most appropriate quantization parameter (QP) to maintain targeted end-to-end delay by jointly considering the current buffer status, network bandwidth and the variation of bit rate of the input video source. Next, in macroblock-layer (MB), a greed-based rate-distortion (R-D) refinement of MBs approach is proposed to efficiently utilize the remaining available bit rate in order to enhance visual quality as much as possible. The experiments show that our two-layer characteristic-based rate control framework can quickly smooth out the impact of varying bit rate of video source on the buffer, meet targeted buffer delay time well, and substantially enhance the quality of the perceived video with lower computational complexity.

Towards energy-efficient streaming system for mobile hotspots

We develop a q -domain characteristic-based rate model for video transmission. Specifically, we extract three characteristics from the quantized discrete cosine transform spectra to construct an accurate rate-quantization (R-Q) model. Moreover, the extraction of the proposed characteristics requires only simple additive operations (additions, subtractions, and comparisons) and few multiplications. Based on the q-domain characteristic-based bitrate model, we develop an efficient two-level greedy-based rate controller for low delay video transmission. Extensive experimental results show that due to the efficiency and accurate estimation of both macroblock and frame-level R-Q models, the proposed rate controller better maintains the source bitrate to meet a predefined buffer delay time, and substantially enhances visual quality as compared with other rate controllers.

A Two-Layer Characteristic-based Rate Control Framework for Low Delay Video Transmission

Modern mobile devices have become an important part of our daily life but the performance of multimedia applications on mobile devices still suffers from the constrained energy supply and shared cellular link bandwidth. In this work, we design a cooperative energy-efficient P2P streaming system to achieve better Quality-of-Experience for a mobile user. First, we formulate and analyze the problem of Maximizing the Lifetime for a mobile P2p Streaming system (MLPS). Then, our distributed bit rate-aware entrust algorithm, jointly considering characteristics of mobile devices and steaming services, is proposed. The simulation results show our scheme can improve the system lifetime by 8 times, and provide more than 2 times throughput than the conventional 3G system does.

Accurate Bitrate Model and Greedy-Based Rate Controller for Low Delay Video Transmission

During recent years, the success of live swarm-based P2P streaming system has been witnessed. Nevertheless, how to design an effective mechanism for mitigating video quality degradation in a lossy network environment is still not thoroughly resolved yet. Unlike conventional client-server paradigm, there is data availability problem in swarm-based P2P streaming system. If we directly conduct the importance-first scheduling strategy (i.e. let the chunks with most distortion-rate efficiency get scheduled first) in swarm-based P2P streaming system, the serious content bottleneck for low priority chunks occurs, particularly when the population size is large. To cope with the above issue, in this work, we propose a dynamic strategy-switching approach that combines the advantages of random scheduling and importance-first scheduling. Our simulation results indicate that compared with existing approaches our approach not only provides better scheduling efficiency, but also is scalable even if population size is large.

On Cooperative Energy-Efficient P2P Live Streaming System for Mobile Hotspots

During recent years, the success of live swarm-based P2P streaming system has been witnessed. Nevertheless, how to design an effective mechanism for mitigating video quality degradation in a lossy network environment is still not thoroughly resolved yet. Unlike conventional client-server paradigm, there is data availability problem in swarm-based P2P streaming system. If we directly conduct the importance-first scheduling strategy in swarm-based P2P streaming system, the serious content bottleneck for low priority chunks occurs, particularly when the population size is large. In this work, we propose a dynamic strategy-switching approach that combines the advantages of random scheduling and importance first scheduling to deal with the problem. Simulation results indicate that our approach not only provides better scheduling efficiency, but also is scalable even if population size is large.

Towards quality-oriented scheduling for live swarm-based P2P streaming

In this paper, we analyze the limitation of ρ-domain based rate-quantization (R-Q) model. We find out that a characteristic-based R-Q model can be derived from ρ-domain to q-domain. Experimental data show that such a characteristic-based R-Q model can provide a more accurate estimation of the actual bitrate than existing models for both frame-level and macroblock(MB)-level. In addition, a simple analysis of computational complexity of our quantization-free characteristics extraction framework shows that our model is faster than existing variance and ρ-domain based R-Q models.

Striking the Balance between Content Diversity and Content Importance in Swarm-Based P2P Streaming System

How to perform rate control on emerging H.264/AVC, which includes the non-normative but widely accepted rate-distortion optimization (RDO) process, i.e., the rate-constrained motion estimation and mode decision, has become a challenging and difficult research issue. Usually, rate controller is useful at given residual signals to determine quantization parameter (QP). However, to perform RDO, a QP should first be predetermined. This leads to a classical chicken and egg dilemma. Therefore, how to understand and predict the coding behavior after RDO with different QPs is the core problem of the H.264/AVC rate controller. In this work, we attempt to adopt coding behaviors of customized residues that are extracted from part of RDO to forward estimate the coding behavior of RDO with different QPs. Experiments show that the proposed rate-quantization (R-Q) model is able to provide a more accurate estimation of the actual bit rate than existing MAD-based model in JM 10.2.

A fast and accurate characteristic-based rate-quantization model for video transmission

With successful deployments of commercial swarm-based P2P streaming systems, peer-to-peer streaming traffic grows rapidly in the last few years. To leverage system-wide bandwidth, peers in P2P streaming systems are expected to exchange their own data with other peers, and this is extremely vulnerable to pollution attacks, where malicious peers inject fake data blocks into the network and those fake data blocks will soon be propagated by the innocent nodes received them. Such attack leads to degradation of data delivery ratio and failure to decode the streaming data. In this paper, we propose a novel Social-Enhanced hash-based Cross-verification framework (SEC) to defense content pollution attacks. That is, the fake data is able to be rapidly identified through the SEC scheme, and thus the malicious nodes are isolated from clean peers. With the assistance of social networks, we can reinforce the resilience of our SEC scheme for multiple colluding malicious peers and improve the data availability by the AIMD-based reputation module. The results show that the SEC framework is able to provide high delivery ratio, (as well as high accuracy, low computational overhead, low communication overhead, and low delay) for fulfilling the stringent requirements of P2P live streaming systems even under a severe or dynamic environment (e.g., when the total capacity of polluters is larger than the source node capacity).