Augmenting mobile 3G using WiFi

Balasubramanian, Aruna; Mahajan, Ratul; Venkataramani, Arun

doi:10.1145/1814433.1814456

Cited by 492 publications

(377 citation statements)

References 17 publications

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“…The high mobility of vehicles makes the accurate prediction of WiFi availability and performance difficult, even when priori knowledge about AP locations and performance or the drive history of vehicles is available. The prediction error in [40] is about 20% when estimate the future 100 s, while in [36], the 90% confidence interval of the prediction of vehicle moving distance for the future 100 s is 150 m in expressway scenario. To optimize offloading performance, more accurate prediction mechanisms, especially for long prediction interval, are then necessary for designing offloading schemes.…”

Section: Future Workmentioning

confidence: 98%

“…There have been several vehicular offloading schemes proposed in the literature [40,36,41]. In [40], an offloading scheme called Wiffler is proposed to determine whether to defer applications for the WiFi connectivity instead of using cellular networks right away.…”

Section: Vehicular Wifi Offloadingmentioning

confidence: 99%

“…In [40], an offloading scheme called Wiffler is proposed to determine whether to defer applications for the WiFi connectivity instead of using cellular networks right away. Wiffler incorporates the prediction of the WiFi throughput on vehicles' route and considers delay requirements of different types of applications.…”

Section: Vehicular Wifi Offloadingmentioning

confidence: 99%

See 2 more Smart Citations

Vehicular WiFi offloading: Challenges and solutions

Cheng

Liu

Zhang

et al. 2014

Vehicular Communications

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WiFi offloading is envisioned as a promising solution to the mobile data explosion problem in cellular networks. WiFi offloading for moving vehicles, however, poses unique characteristics and challenges, due to high mobility, fluctuating mobile channels, etc. In this paper, we focus on the problem of WiFi offloading in vehicular communication environments. Specifically, we discuss the challenges and identify the research issues related to drive-thru Internet access and effectiveness of vehicular WiFi offloading. Moreover, we review the state-of-the-art offloading solutions, in which advanced vehicular communications can be employed. We also shed some lights on the path for future research on this topic.

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Section: Future Workmentioning

confidence: 98%

Section: Vehicular Wifi Offloadingmentioning

confidence: 99%

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Vehicular WiFi offloading: Challenges and solutions

Cheng

Liu

Zhang

et al. 2014

Vehicular Communications

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show abstract

“…Motivated by these potential economic benefits, thus far efficient offloading mechanisms have been proposed. Wiffler [38] was developed to augment third-generation (3G) network capacity for delay-tolerant applications over vehicular networks. MultiNets [39] is a seamless offloading system that allows a mobile device to automatically switch to the most suitable network interface based on user-defined policies such as energy saving and throughput.…”

Section: Related Workmentioning

confidence: 99%

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

Park

Song

2018

IEEE Trans. on Mobile Comput.

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Abstract-This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the ε-Nash equilibrium.Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.

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“…Also, it relies on active probing to determine if links are available and user interaction to switch between links. Another example is Wiffler [1], where the main idea is to delay transfers in order to save money. Applications that send data specify their delay threshold, and the solution makes use of a proxy, switches between 3G and WLAN and tries to predict WLAN-availability.…”

Section: Related Workmentioning

confidence: 99%

Mobile video streaming using location-based network prediction and transparent handover

Evensen

Petlund

Riiser³

et al. 2011

Proceedings of the 21st International Workshop on Network and Operating Systems Support for Digital Audio and Video

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A well known challenge with mobile video streaming is fluctuating bandwidth. As the client devices move in and out of network coverage areas, the users may experience varying signal strengths, competition for the available resources and periods of network outage. These conditions have a significant effect on video quality.In this paper, we present a video streaming solution for roaming clients that is able to compensate for the effects of oscillating bandwidth through bandwidth prediction and video quality scheduling. We combine our existing adaptive segmented HTTP streaming system with 1) an application layer framework for creating transparent multi-link applications, and 2) a location-based QoS information system containing GPS coordinates and accompanying bandwidth measurements, populated through crowd-sourcing. Additionally, we use real-time traffic information to improve the prediction by, for example, estimating the length of a commute route. To evaluate our prototype, we performed realworld experiments using a popular tram route in Oslo, Norway. The client connected to multiple networks, and the results show that our solution increases the perceived video quality significantly. Also, we used simulations to evaluate the potential of aggregating bandwidth along the route.

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Augmenting mobile 3G using WiFi

Cited by 492 publications

References 17 publications

Vehicular WiFi offloading: Challenges and solutions

Vehicular WiFi offloading: Challenges and solutions

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

Mobile video streaming using location-based network prediction and transparent handover

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