本文選題：自適應(yīng)視頻流 + 移動(dòng)云計(jì)算�。� 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：近年來,云計(jì)算領(lǐng)域新技術(shù)層出不窮且呈現(xiàn)不斷融合的趨勢(shì),云媒體技術(shù)日益成熟,提供高質(zhì)量多樣化的多媒體解決方案成為了可能。如何滿足用戶對(duì)在存儲(chǔ)、計(jì)算能力有限以及電量有限的手持設(shè)備上享受高質(zhì)量和多樣化云多媒體服務(wù)的需求仍然是一項(xiàng)有趣和具有挑戰(zhàn)性的研究。怎樣合理并且高效地利用云資源及移動(dòng)終端資源,實(shí)現(xiàn)低成本、高能效并滿足服務(wù)質(zhì)量需求的資源供應(yīng)和服務(wù)提供,是云視頻服務(wù)系統(tǒng)中不可避免的且具有實(shí)際意義的熱點(diǎn)問題。本文結(jié)合云計(jì)算技術(shù)和傳統(tǒng)的自適應(yīng)視頻流技術(shù),對(duì)基于云計(jì)算的自適應(yīng)視頻流傳輸框架、移動(dòng)終端高能效的分辨率碼率調(diào)度策略、成本優(yōu)化和服務(wù)質(zhì)量(Quality of Service,QoS)保證的動(dòng)態(tài)資源調(diào)度三個(gè)方面進(jìn)行了研究,主要工作如下:(1)研究基于云計(jì)算的自適應(yīng)視頻流傳輸系統(tǒng)的架構(gòu)設(shè)計(jì)問題。傳統(tǒng)的DASH(Dynamic Adaptive Streaming over HTTP)系統(tǒng)實(shí)現(xiàn)自適應(yīng)視頻流所需要的計(jì)算以及緩存工作都必須在終端上進(jìn)行,終端設(shè)備的能耗巨大。因此本文借助云計(jì)算技術(shù)強(qiáng)大的計(jì)算能力和海量存儲(chǔ)能力,把部分計(jì)算遷移至云計(jì)算中心,研究基于云計(jì)算的自適應(yīng)視頻流傳輸系統(tǒng)的設(shè)計(jì)。系統(tǒng)實(shí)現(xiàn)了把移動(dòng)終端的信息實(shí)時(shí)傳輸?shù)皆贫?云端根據(jù)移動(dòng)終端的網(wǎng)絡(luò)能力和實(shí)時(shí)能耗進(jìn)行策略評(píng)估,并對(duì)原始視頻進(jìn)行實(shí)時(shí)轉(zhuǎn)碼后再傳送到移動(dòng)終端。(2)在系統(tǒng)框架的基礎(chǔ)上,研究基于終端能耗感知的分辨率碼率自適應(yīng)算法。參考了現(xiàn)有研究成果,首先對(duì)智能手機(jī)主要耗電部件進(jìn)行逐一詳細(xì)的分析,并進(jìn)一步分析了視頻流式處理過程中各個(gè)環(huán)節(jié)智能手機(jī)的能耗情況;其次建立了智能終端設(shè)備耗電因素和視頻編解碼參數(shù)之間的數(shù)學(xué)模型;最后提出了改進(jìn)的基于終端能耗感知的分辨率碼率自適應(yīng)算法以求解數(shù)學(xué)模型。實(shí)驗(yàn)結(jié)果表明,相比DASH自適應(yīng)視頻流策略,本文算法能夠依據(jù)終端設(shè)備能耗變化來自適應(yīng)的調(diào)節(jié)視頻分辨率和碼率,有效降低了終端設(shè)備的能耗。(3)在系統(tǒng)框架基礎(chǔ)上,研究面向云端視頻轉(zhuǎn)碼的基于成本的資源自適應(yīng)配置算法。實(shí)現(xiàn)自適應(yīng)視頻流技術(shù)必然離不開云環(huán)境下的實(shí)時(shí)轉(zhuǎn)碼,本文針對(duì)海量視頻轉(zhuǎn)碼需求給視頻服務(wù)供應(yīng)商帶來的巨大資本壓力問題,首先建立QoS模型;其次,將IaaS云平臺(tái)的虛擬機(jī)動(dòng)態(tài)供應(yīng)問題建模成實(shí)時(shí)轉(zhuǎn)碼過程中需要激活的虛擬機(jī)數(shù)量的最小化問題;最后通過本文提出的成本優(yōu)化和服務(wù)質(zhì)量保證的自適應(yīng)資源調(diào)度策略對(duì)虛擬機(jī)的數(shù)量進(jìn)行動(dòng)態(tài)調(diào)整。實(shí)驗(yàn)結(jié)果表明,本文所提出的算法相比傳統(tǒng)的資源調(diào)度方法不僅降低了視頻服務(wù)供應(yīng)商的成本和服務(wù)器過載概率,還提高了資源平均利用率和服務(wù)質(zhì)量。
[Abstract]:In recent years, new technologies in the field of cloud computing emerge in endlessly and show a trend of continuous integration. Cloud media technology is becoming more and more mature, and it is possible to provide high-quality and diversified multimedia solutions. How to meet users' demand for high-quality and diversified cloud multimedia services on handheld devices with limited storage, computing power and limited power remains an interesting and challenging study. How to make rational and efficient use of cloud resources and mobile terminal resources to achieve low cost, high energy efficiency and meet the quality of service demand for resource supply and service delivery, It is an inevitable and practical hot issue in cloud video service system. Combined with cloud computing technology and traditional adaptive video stream technology, this paper analyzes the adaptive video stream transmission framework based on cloud computing and the high resolution rate scheduling strategy for mobile terminals. Cost optimization and dynamic resource scheduling guaranteed by quality of Service (QoS) are studied. The main work is as follows: 1) the architecture design of adaptive video streaming system based on cloud computing is studied. The traditional DASHN dynamic Adaptive streaming over (DASH) system has to perform the computing and caching work on the terminal for adaptive video stream, and the terminal equipment has a huge energy consumption. Therefore, with the powerful computing power and mass storage capacity of cloud computing technology, this paper migrates part of computing to cloud computing center, and studies the design of adaptive video stream transmission system based on cloud computing. The system realizes the real-time transmission of mobile terminal information to the cloud. The cloud evaluates the strategy according to the network capability and real-time energy consumption of the mobile terminal. The original video is transcoded in real time and then transmitted to the mobile terminal. On the basis of the system framework, the resolution rate adaptive algorithm based on terminal energy consumption sensing is studied. Referring to the existing research results, the main components of smart phone power consumption are analyzed one by one, and the energy consumption of each link in the process of video flow processing is further analyzed. Secondly, the mathematical model between the power consumption factor of intelligent terminal equipment and the video coding and decoding parameters is established. Finally, an improved adaptive algorithm based on terminal energy consumption perception is proposed to solve the mathematical model. The experimental results show that compared with the DASH adaptive video stream strategy, the algorithm can adaptively adjust the video resolution and bit rate according to the energy consumption change of the terminal equipment, and effectively reduce the energy consumption of the terminal equipment on the basis of the system framework. A cost-based adaptive resource allocation algorithm for cloud video transcoding is studied. The realization of adaptive video stream technology is bound to be inseparable from the real-time transcoding in the cloud environment. This paper aims at the huge capital pressure caused by the mass video transcoding requirements to the video service providers, first, establishes the QoS model; secondly, The dynamic provisioning of virtual machines in the IaaS cloud platform is modeled as the minimization of the number of virtual machines that need to be activated in the real-time transcoding process. Finally, the number of virtual machines is dynamically adjusted by the adaptive resource scheduling strategy of cost optimization and quality of service assurance proposed in this paper. Experimental results show that the proposed algorithm not only reduces the cost of video service providers and server overload probability, but also improves the average resource utilization and quality of service compared with the traditional resource scheduling method.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN919.8

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