Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.Comment: 7 pages, 15 figure

Time-Shifted Prefetching and Edge-Caching of Video Content: Insights, Algorithms, and Solutions

Video traffic accounts for 82% of global Internet traffic and is growing at an unprecedented rate. As a result of this rapid growth and popularity of video content, the network is heavily burdened. To cope with this, service providers have to spend several millions of dollars for infrastructure upgrades; these upgrades are typically triggered when there is a reasonably sustained peak usage that exceeds 80% of capacity. In this context, with network traffic load being significantly higher during peak periods (up to 5 times as much), we explore the problem of prefetching video content during off-peak periods of the network even when such periods are substantially separated from the actual usage-time. To this end, we collected YouTube and Netflix usage from over 1500 users spanning at least a one-year period consisting of approximately 8.5 million videos collectively watched. We use the datasets to analyze and present key insights about user-level usage behavior, and show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. Thereafter, equipped with the datasets and our derived insights, we develop a set of data-driven prediction and prefetching solutions, using machine-learning and deep-learning techniques (specifically supervised classifiers and LSTM networks), which anticipates the video content the user will consume based on their prior watching behavior, and prefetches it during off-peak periods. We find that our developed solutions can reduce nearly 35% of peak-time YouTube traffic and 70% of peak-time Netflix series traffic. We developed and evaluated a proof-of-concept system for prefetching video traffic. We also show how to integrate the two systems for prefetching YouTube and Netflix content. Furthermore, based on our findings from our developed algorithms, we develop a framework for prefetching video content regardless of the type of video and platform upon which it is hosted.Ph.D

Quality of experience and access network traffic management of HTTP adaptive video streaming

The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

Collaborative Traffic Offloading for Mobile Systems

Due to the popularity of smartphones and mobile streaming services, the growth of traffic volume in mobile networks is phenomenal. This leads to huge investment pressure on mobile operators' wireless access and core infrastructure, while the profits do not necessarily grow at the same pace. As a result, it is urgent to find a cost-effective solution that can scale to the ever increasing traffic volume generated by mobile systems. Among many visions, mobile traffic offloading is regarded as a promising mechanism by using complementary wireless communication technologies, such as WiFi, to offload data traffic away from the overloaded mobile networks. The current trend to equip mobile devices with an additional WiFi interface also supports this vision. This dissertation presents a novel collaborative architecture for mobile traffic offloading that can efficiently utilize the context and resources from networks and end systems. The main contributions include a network-assisted offloading framework, a collaborative system design for energy-aware offloading, and a software-defined networking (SDN) based offloading platform. Our work is the first in this domain to integrate energy and context awareness into mobile traffic offloading from an architectural perspective. We have conducted extensive measurements on mobile systems to identify hidden issues of traffic offloading in the operational networks. We implement the offloading protocol in the Linux kernel and develop our energy-aware offloading framework in C++ and Java on commodity machines and smartphones. Our prototype systems for mobile traffic offloading have been tested in a live environment. The experimental results suggest that our collaborative architecture is feasible and provides reasonable improvement in terms of energy saving and offloading efficiency. We further adopt the programmable paradigm of SDN to enhance the extensibility and deployability of our proposals. We release the SDN-based platform under open-source licenses to encourage future collaboration with research community and standards developing organizations. As one of the pioneering work, our research stresses the importance of collaboration in mobile traffic offloading. The lessons learned from our protocol design, system development, and network experiments shed light on future research and development in this domain.Yksi mobiiliverkkojen suurimmista haasteista liittyy liikennemäärien eksponentiaaliseen kasvuun. Tämä verkkoliikenteen kasvu johtuu pitkälti suosituista videopalveluista, kuten YouTube ja Netflix, jotka lähettävät liikkuvaa kuvaa verkon yli. Verkon lisääntynyt kuormitus vaatii investointeja verkon laajentamiseksi. On tärkeää löytää kustannustehokkaita tapoja välittää suuressa mittakaavassa sisältöä ilman mittavia infrastruktuuri-investointeja. Erilaisia liikennekuormien ohjausmenetelmiä on ehdotettu ratkaisuksi sisällönvälityksen tehostamiseen mobiiliverkoissa. Näissä ratkaisuissa hyödynnetään toisiaan tukevia langattomia teknologioita tiedonvälityksen tehostamiseen, esimerkiksi LTE-verkosta voidaan delegoida tiedonvälitystä WiFi-verkoille. Useimmissa kannettavissa laitteissa on tuki useammalle langattomalle tekniikalle, joten on luonnollista hyödyntää näiden tarjoamia mahdollisuuksia tiedonvälityksen tehostamisessa. Tässä väitöskirjassa tutkitaan liikennekuormien ohjauksen toimintaa ja mahdollisuuksia mobiiliverkoissa. Työssä esitetään uusi yhteistyöpohjainen liikennekuormien ohjausjärjestelmä, joka hyödyntää päätelaitteiden ja verkon tilannetietoa liikennekuormien optimoinnissa. Esitetty järjestelmä ja arkkitehtuuri on ensimmäinen, joka yhdistää energiankulutuksen ja kontekstitiedon liikennekuormien ohjaukseen. Väitöskirjan keskeisiä tuloksia ovat verkon tukema liikennekuormien ohjauskehikko, yhteistyöpohjainen energiatietoinen optimointiratkaisu sekä avoimen lähdekoodin SoftOffload-ratkaisu, joka mahdollistaa ohjelmistopohjaisen liikennekuormien ohjauksen. Esitettyjä järjestelmiä arvioidaan kokeellisesti kaupunkiympäristöissä älypuhelimia käyttäen. Työn tulokset mahdollistavat entistä energiatehokkaammat liikennekuormien ohjausratkaisut ja tarjoavat ideoita ja lähtökohtia tulevaan 5G kehitystyöhön

처리율과 지연시간의 트레이드오프를 통한 비용 인지 데이터 오프로딩

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 권태경.최근의 모바일 데이터 수요의 급격한 증가에 대처하기 위해, 무선 인터넷 서비스 제공자들(ISPs)은 새로운 요금제를 점차 도입하고 있으며, 모바일 트래픽을 오프로딩 하기 위해 WiFi 핫스팟을 설치하고 있다. 하지만, 이러한 인터넷 서비스 제공자 중심의 트래픽 관리를 위한 방안들은 모바일 사용자들의 이익과 항상 일치하는 것은 아니다. 사용자들은 그들의 오프로딩 결정을 위해 비용, 처리율, 지연시간 간의 복잡하고 다차원적인 트레이드오프(tradeoff)에 직면하게 된다. 즉, WiFi를 사용하기 위해 기다림으로써 비용을 절약하고 높은 처리율을 제공받을 수 있지만, 지연시간에 민감한 사용자의 경우 WiFi가 접근 가능할 때까지 기다리지 않을 수 있다. 이러한 트레이드오프를 처리하기 위해 우리는 사용자의 처리율, 지연시간 트레이드오프와 데이터 예산 상의 제약을 고려하는 실용적인 비용인지 WiFi 오프로딩 시스템의 기능적 프로토타입인 AMUSE(Adaptive bandwidth Management through USer-Empowerment, 사용자 중심의 적응적 대역폭 관리기법)를 제안한다. 예측된 미래의 데이터 사용량과 WiFi 이용가능 여부를 바탕으로, AMUSE는 어떠한 어플리케이션을 하루 중 어떤 시간으로 오프로딩 할지를 결정한다. 또한 모바일 장치의 대부분의 트래픽이 TCP 트래픽이기 때문에, 각 TCP 어플리케이션의 할당된 레이트(rate)를 적용하기 위한 새로운 수신자 기반 대역폭 할당 기법을 제시한다. 따라서, AMUSE는 다양한 어플리케이션 컨텐트 서버의 도움 없이 비용-처리율-지연시간 트레이드오프에 따라 대역폭 할당을 최적화할 수 있다. 20명의 스마트폰 사용자의 트래픽 사용량 데이터에 대한 측정 연구를 통해, 사용자들은 몇몇 종류의 어플리케이션에 대해 트래픽의 많은 부분을 이미 오프로딩 하고 있지만, 우리의 기법을 사용하여 이동통신 트래픽의 상당 부분을 추가적으로 오프로딩 할 수 있음을 발견하였다. 우리는 AMUSE를 Windows 7 테블릿 상에 구현하고, 37명의 모바일 사용자로부터 얻은 3G 및 WiFi 사용량 데이터를 통해 AMUSE의 성능을 평가하였다. 실험 결과는 AMUSE가 사용자의 만족도를 향상시킴을 보여준다. AMUSE와 비교해서 다른 오프로딩 알고리즘은 사용량이 적은 사용자와 많은 사용자에 대해 각각 14% 와 27% 낮은 사용자 만족도를 보여준다. 결론적으로, 비용, 처리율, 지연시간에 대한 사용자의 상충된 이해관계를 지능적으로 관리함으로써 오프로딩 결정을 향상시킬 수 있음을 알 수 있다.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Empowering User Decisions . . . . . . . . . . . . . . . . . . . . . 1 1.2 Components of AMUSE . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 Bandwidth Optimizer . . . . . . . . . . . . . . . . . . . . . 5 1.2.3 TCP Rate Controller and Session Tracker . . . . . . . . . . 6 II. RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 III. Bandwidth Optimizer . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1 Predicting WiFi Connectivity . . . . . . . . . . . . . . . . . . . . . 12 3.2 Predicting Future Usage . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 User Utility Maximization . . . . . . . . . . . . . . . . . . . . . . 15 3.3.1 Utility Functions . . . . . . . . . . . . . . . . . . . . . . . 15 3.3.2 Users Optimization Problem . . . . . . . . . . . . . . . . 18 3.4 Online Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 IV. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1 Receiver-Side TCP Rate Control . . . . . . . . . . . . . . . . . . . 24 V. Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5.2 Application types . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.3 Offloading practice . . . . . . . . . . . . . . . . . . . . . . . . . . 32 VI. Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 36 6.1 Bandwidth Optimizer . . . . . . . . . . . . . . . . . . . . . . . . . 36 6.1.1 Experimental Data and Settings . . . . . . . . . . . . . . . 36 6.1.2 Baseline Algorithms . . . . . . . . . . . . . . . . . . . . . 40 6.1.3 Numerical Results . . . . . . . . . . . . . . . . . . . . . . 42 6.2 Receiver-side TCP rate control . . . . . . . . . . . . . . . . . . . . 45 6.2.1 Real network experiments . . . . . . . . . . . . . . . . . . 46 6.2.2 Experiments in emulated networks . . . . . . . . . . . . . . 46 VII. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 7.1 Application of AMUSE in various data plans . . . . . . . . . . . . 58 7.2 Overhead of location sensing . . . . . . . . . . . . . . . . . . . . . 59 VIII. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Korean Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Docto

Effective and Efficient Communication and Collaboration in Participatory Environments

Participatory environments pose significant challenges to deploying real applications. This dissertation investigates exploitation of opportunistic contacts to enable effective and efficient data transfers in challenged participatory environments. There are three main contributions in this dissertation: 1. A novel scheme for predicting contact volume during an opportunistic contact (PCV); 2. A method for computing paths with combined optimal stability and capacity (COSC) in opportunistic networks; and 3. An algorithm for mobility and orientation estimation in mobile environments (MOEME). The proposed novel scheme called PCV predicts contact volume in soft real-time. The scheme employs initial position and velocity vectors of nodes along with the data rate profile of the environment. PCV enables efficient and reliable data transfers between opportunistically meeting nodes. The scheme that exploits capacity and path stability of opportunistic networks is based on PCV for estimating individual link costs on a path. The total path cost is merged with a stability cost to strike a tradeoff for maximizing data transfers in the entire participatory environment. A polynomial time dynamic programming algorithm is proposed to compute paths of optimum cost. We propose another novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME), as prediction of user movement paves way for efficient data transfers, resource allocation and event scheduling in participatory environments. MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. Indeed, accurate prediction of contact volume, path capacity and stability and user movement can improve performance of deployments. However, existing schemes for such estimations make use of preconceived patterns or contact time distributions that may not be applicable in uncertain environments. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets