運用與提升多媒體應用以增進生活便利為近來技術發展主流，人們利用新穎技術運用各種領域並達成不同目的。人們為了尋求更方便更聰明地生活型態，便研究分析自己的生活、行動、行為模式，引領相關智慧型空間的研究發展。除了連結家庭中的各種資訊與媒體的內部網路，也需將媒體互動的功能在網際網路間實現。本論文之媒體共享管理平台整合之三種模組功能：(一) 整合多媒體串流傳輸與數位保護關鍵技術，本功能透過整合內容傳遞網路(Content Delivery Network, CDN)與點對點(Peer-to-Peer, P2P)傳輸架構，針對多參數考量以動態調整群播樹(Multi-Parametered Adaptive Tree, MPAT)達穩定傳輸與減少節點斷線頻率；(二) 基於前項網路媒體系統，我們開發依收視群喜好推薦節目之互動電視系統，以人機介面辨識使用者身分，針對使用者個人及群體進行不同之推薦組合，採混合式推薦(hybrid recommender)法結合內容導向(Content-Based, CB)及協合過濾(Collaborative Filtering, CF)法之優點。個人化推薦模組更可透過更新演算法，利用個人觀看歷史記錄逐次更新使用者喜好；(三) 運用於智慧空間之無線射頻辨識(Radio Frequency Identification, RFID)之即時定位系統(Real-Time Location System, RTLS)，設計分群演算法(LOCate TRacking tag through rEader power control and Candidate region intersection, LOCTREC)以減少辨識參考點，接著使用攝影機擷取場景影像讓使用者得知物體情境。實驗結果顯示：(一) 所設計用於P2P-IPTV之MPAT演算法確實可以減少傳輸之延遲，並提升媒體群播樹傳輸之穩定度；(二) 我們所設計的混合式推薦模組可以更準確的預測群體對節目的評比，且在訓練過程中，選擇適當的學習速率將有利於訓練權重值之精確度；(三) LOCTREC方法可以有效濾除無相關之參考標籤，以增加定位準確度。本論文所研究開發的相關技術，在於提供使用者一個不受時、空，以及裝置限制的媒體及物件之存取環境。

With the advance of multimedia codec technologies and Internet prevalence, multimedia has been regarded as one of the major information communication tools. Development of a sharing management platform focused on the integrating various media information into a single user interface. In this thesis, three modules have been developed according to different application requirements: 1) Developing a Peer-to-Peer IPTV (P2P) to provide network media service. To improve the reliability of an IPTV multicast tree, a Multi-Parametered Adaptive Tree (MPAT) algorithm is proposed to reduce the frequency of peer disruption. A DRM server and IPMP-Terminal have also been developed to provide a complete P2P-IPTV service platform; 2) Based on the IPTV platform, an efficient front end TV program recommender has been developed. We proposed to estimate inter-user dominance factor through the neural network algorithm, based on practical group user rating records. Both Content-Based (CB) and user-based Collaborative Filtering (CF) algorithms are adopted to predict group users’ preference for program recommendation. In addition, an active face recognition module has been developed and integrated with the recommender system to provide a user-friendly interface; 3) For smart space applications, a Real-Time Location Systems (RTLS) based on Radio Frequency Identification (RFID) techniques is proposed. A new control method, LOCate TRacking tag through rEader power control and Candidate region intersection, LOCTREC, has been proposed to improve the RTLS estimation accuracy. Multi-readers were operated with multi-power level control to progressively refine the target tag region for accurate location estimation. Experiments showed that: 1) Compared to previous researches, the practical P2P-IPTV system is carried out based on theoretical analysis to improve perception QoS. The proposed P2P routing strategy can reduce the transmission delay, and improve the reliability of a multicast tree; 2) The proposed group user program recommender can achieve higher accuracy in recommending video programs for group users, in additional to a user-friendly recommendation function; 3) Experiments showed that the estimation error of the proposed LOCTREC is 70% smaller than its counterpart of the LANDMARC which deploys regularly spaced reference tags to determine signal strength. The developed system methods in this thesis enable users to access to information any-time, any-place and through any-devices.

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