Popularity-Based Adaptive Content Delivery Scheme with In-Network Caching

To solve the increasing popularity of video streaming services over the Internet, recent research activities have addressed the locality of content delivery from a network edge by introducing a storage module into a router. To employ in-network caching and persistent request routing, this paper introduces a hybrid content delivery network (CDN) system combining novel content routers in an underlay together with a traditional CDN server in an overlay. This system first selects the most suitable delivery scheme (that is, multicast or broadcast) for the content in question and then allocates an appropriate number of channels based on a consideration of the content’s popularity. The proposed scheme aims to minimize traffic volume and achieve optimal delivery cost, since the most popular content is delivered through broadcast channels and the least popular through multicast channels. The performance of the adaptive scheme is clearly evaluated and compared against both the multicast and broadcast schemes in terms of the optimal in-network caching size and number of unicast channels in a content router to observe the significant impact of our proposed scheme

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Video applications normally demand stringent quality-of-service (QoS) for the high quality and smooth video playback at the receiver. Since the network is usually shared by multiple applications with diverse QoS requirements, QoS provisioning is an important and difficult task for the efficient and smooth video delivery. In the context of cognitive radio (CR) networks, as the secondary or unlicensed users share a pool of bandwidth that is temporarily being unused by the primary or licensed users, there is an inevitable interference between the licensed primary users and the unlicensed CR devices. As a result, efficient and smooth video delivery becomes even more challenging as the channel spectrum is not only a precious resource, but also much more dynamic and intermittently available to secondary users. In this thesis, we focus on the provision of guaranteed QoS to video streaming subscribers in CR network. In video streaming applications, a playout buffer is typically deployed at the receiver to deal with the impact of the network dynamics. With different buffer storage, users can have different tolerance to the network dynamics. We exploit this feature for channel allocation in CR network. To this end, we model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results demonstrate that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback

A Scalable Solution For Interactive Video Streaming

This dissertation presents an overall solution for interactive Near Video On Demand (NVOD) systems, where limited server and network resources prevent the system from servicing all customers’ requests. The interactive nature of recent workloads complicates matters further. Interactive requests require additional resources to be handled. This dissertation analyzes the system performance under a realistic workload using different stream merging techniques and scheduling policies. It considers a wide range of system parameters and studies their impact on the waiting and blocking metrics. In order to improve waiting customers experience, we propose a new scheduling policy for waiting customers that is fairer and delivers a descent performance. Blocking is a major issue in interactive NVOD systems and we propose a few techniques to minimize it. In particular, we study the maximum Interactive Stream (I-Stream) length (Threshold) that should be allowed in order to prevent a few requests from using the expensive I-Streams for a prolonged period of time, which starves other requests from a chance of using this valuable resource. Using a reasonable I-Stream threshold proves very effective in improving blocking metrics. Moreover, we introduce an I-Stream provisioning policy to dynamically shift resources based on the system requirements at the time. The proposed policy proves to be highly effective in improving the overall system performance. To account for both average waiting time and average blocking time, we introduce a new metric (Aggregate Delay) . We study the client-side cache management policy. We utilize the customer’s cache to service most interactive requests, which reduces the load on the server. We propose three purging algorithms to clear data when the cache gets full. Purge Oldest removes the oldest data in the cache, whereas Purge Furthest clears the furthest data from the client’s playback point. In contrast, Adaptive Purge tries to avoid purging any data that includes the customer’s playback point or the playback point of any stream that is being listened to by the client. Additionally, we study the impact of the purge block, which is the least amount of data to be cleared, on the system performance. Finally, we study the effect of bookmarking on the system performance. A video segment that is searched and watched repeatedly is called a hotspot and is pointed to by a bookmark. We introduce three enhancements to effectively support bookmarking. Specifically, we propose a new purging algorithm to avoid purging hotspot data if it is already cached. On top of that, we fetch hotspot data for customers not listening to any stream. Furthermore, we reserve multicast channels to fetch hotspot data

Maximizing Resource Utilization In Video Streaming Systems

Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to utilize include server bandwidth, network bandwidth, battery life in battery operated devices, and processing time in limited processing power devices. In this work, we propose new techniques to maximize the utilization of video-on-demand (VOD) server resources. In addition to that, we propose new framework to maximize the utilization of the network bandwidth in wireless video streaming systems. Providing video streaming users in a VOD system with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait thereby increasing server utilization by increasing server throughput. In this work, we analyze waiting-time predictability in scalable video streaming. We also propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. The achieved resource sharing by stream merging depends greatly on how the waiting requests are scheduled for service. Motivated by the development of cost-based scheduling, we investigate its effectiveness in great detail and discuss opportunities for further tunings and enhancements. Additionally, we analyze the effectiveness of incorporating video prediction results into the scheduling decisions. We also study the interaction between scheduling policies and the stream merging techniques and explore new ways for enhancements. The interest in video surveillance systems has grown dramatically during the last decade. Auto-mated video surveillance (AVS) serves as an efficient approach for the realtime detection of threats and for monitoring their progress. Wireless networks in AVS systems have limited available bandwidth that have to be estimated accurately and distributed efficiently. In this research, we develop two cross-layer optimization frameworks that maximize the bandwidth optimization of 802.11 wireless network. We develop a distortion-based cross-layer optimization framework that manages bandwidth in the wire-less network in such a way that minimizes the overall distortion. We also develop an accuracy-based cross-layer optimization framework in which the overall detection accuracy of the computer vision algorithm(s) running in the system is maximized. Both proposed frameworks manage the application rates and transmission opportunities of various video sources based on the dynamic network conditions to achieve their goals. Each framework utilizes a novel online approach for estimating the effective airtime of the network. Moreover, we propose a bandwidth pruning mechanism that can be used with the accuracy-based framework to achieve any desired tradeoff between detection accuracy and power consumption. We demonstrate the effectiveness of the proposed frameworks, including the effective air-time estimation algorithms and the bandwidth pruning mechanism, through extensive experiments using OPNET

Design and analysis of stream scheduling algorithms in distributed reservation-based multimedia systems

Ph.DDOCTOR OF PHILOSOPH

Enabling Large-Scale Peer-to-Peer Stored Video Streaming Service with QoS Support

This research aims to enable a large-scale, high-volume, peer-to-peer, stored-video streaming service over the Internet, such as on-line DVD rentals. P2P allows a group of dynamically organized users to cooperatively support content discovery and distribution services without needing to employ a central server. P2P has the potential to overcome the scalability issue associated with client-server based video distribution networks; however, it brings a new set of challenges. This research addresses the following five technical challenges associated with the distribution of streaming video over the P2P network: 1) allow users with limited transmit bandwidth capacity to become contributing sources, 2) support the advertisement and discovery of time-changing and time-bounded video frame availability, 3) Minimize the impact of distribution source losses during video playback, 4) incorporate user mobility information in the selection of distribution sources, and 5) design a streaming network architecture that enables above functionalities.To meet the above requirements, we propose a video distribution network model based on a hybrid architecture between client-server and P2P. In this model, a video is divided into a sequence of small segments and each user executes a scheduling algorithm to determine the order, the timing, and the rate of segment retrievals from other users. The model also employs an advertisement and discovery scheme which incorporates parameters of the scheduling algorithm to allow users to share their life-time of video segment availability information in one advertisement and one query. An accompanying QoS scheme allows reduction in the number of video playback interruptions while one or more distribution sources depart from the service prematurely.The simulation study shows that the proposed model and associated schemes greatly alleviate the bandwidth requirement of the video distribution server, especially when the number of participating users grows large. As much as 90% of load reduction was observed in some experiments when compared to a traditional client-server based video distribution service. A significant reduction is also observed in the number of video presentation interruptions when the proposed QoS scheme is incorporated in the distribution process while certain percentages of distribution sources depart from the service unexpectedly

Design, analysis, and experimental verification of continuous media retrieval and caching strategies for network-based multimedia services

Ph.DDOCTOR OF PHILOSOPH

Optimized algorithms for multimedia streaming

Ph.DDOCTOR OF PHILOSOPH

Ontwerp en evaluatie van content distributie netwerken voor multimediale streaming diensten.

Traditionele Internetgebaseerde diensten voor het verspreiden van bestanden, zoals Web browsen en het versturen van e-mails, worden aangeboden via één centrale server. Meer recente netwerkdiensten zoals interactieve digitale televisie of video-op-aanvraag vereisen echter hoge kwaliteitsgaranties (QoS), zoals een lage en constante netwerkvertraging, en verbruiken een aanzienlijke hoeveelheid bandbreedte op het netwerk. Architecturen met één centrale server kunnen deze garanties moeilijk bieden en voldoen daarom niet meer aan de hoge eisen van de volgende generatie multimediatoepassingen. In dit onderzoek worden daarom nieuwe netwerkarchitecturen bestudeerd, die een dergelijke dienstkwaliteit kunnen ondersteunen. Zowel peer-to-peer mechanismes, zoals bij het uitwisselen van muziekbestanden tussen eindgebruikers, als servergebaseerde oplossingen, zoals gedistribueerde caches en content distributie netwerken (CDN's), komen aan bod. Afhankelijk van de bestudeerde dienst en de gebruikte netwerktechnologieën en -architectuur, worden gecentraliseerde algoritmen voor netwerkontwerp voorgesteld. Deze algoritmen optimaliseren de plaatsing van de servers of netwerkcaches en bepalen de nodige capaciteit van de servers en netwerklinks. De dynamische plaatsing van de aangeboden bestanden in de verschillende netwerkelementen wordt aangepast aan de heersende staat van het netwerk en aan de variërende aanvraagpatronen van de eindgebruikers. Serverselectie, herroutering van aanvragen en het verspreiden van de belasting over het hele netwerk komen hierbij ook aan bod

Proactive Mechanisms for Video-on-Demand Content Delivery

Video delivery over the Internet is the dominant source of network load all over the world. Especially VoD streaming services such as YouTube, Netflix, and Amazon Video have propelled the proliferation of VoD in many peoples' everyday life. VoD allows watching video from a large quantity of content at any time and on a multitude of devices, including smart TVs, laptops, and smartphones. Studies show that many people under the age of 32 grew up with VoD services and have never subscribed to a traditional cable TV service. This shift in video consumption behavior is continuing with an ever-growing number of users. satisfy this large demand, VoD service providers usually rely on CDN, which make VoD streaming scalable by operating a geographically distributed network of several hundreds of thousands of servers. Thereby, they deliver content from locations close to the users, which keeps traffic local and enables a fast playback start. CDN experience heavy utilization during the day and are usually reactive to the user demand, which is not optimal as it leads to expensive over-provisioning, to cope with traffic peaks, and overreacting content eviction that decreases the CDN's performance. However, to sustain future VoD streaming projections with hundreds of millions of users, new approaches are required to increase the content delivery efficiency. To this end, this thesis identifies three key research areas that have the potential to address the future demand for VoD content. Our first contribution is the design of vFetch, a privacy-preserving prefetching mechanism for mobile devices. It focuses explicitly on OTT VoD providers such as YouTube. vFetch learns the user interest towards different content channels and uses these insights to prefetch content on a user terminal. To do so, it continually monitors the user behavior and the device's mobile connectivity pattern, to allow for resource-efficient download scheduling. Thereby, vFetch illustrates how personalized prefetching can reduce the mobile data volume and alleviate mobile networks by offloading peak-hour traffic. Our second contribution focuses on proactive in-network caching. To this end, we present the design of the ProCache mechanism that divides the available cache storage concerning separate content categories. Thus, the available storage is allocated to these divisions based on their contribution to the overall cache efficiency. We propose a general work-flow that emphasizes multiple categories of a mixed content workload in addition to a work-flow tailored for music video content, the dominant traffic source on YouTube. Thereby, ProCache shows how content-awareness can contribute to efficient in-network caching. Our third contribution targets the application of multicast for VoD scenarios. Many users request popular VoD content with only small differences in their playback start time which offers a potential for multicast. Therefore, we present the design of the VoDCast mechanism that leverages this potential to multicast parts of popular VoD content. Thereby, VoDCast illustrates how ISP can collaborate with CDN to coordinate on content that should be delivered by ISP-internal multicast