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

Live Prefetching for Mobile Computation Offloading

The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio

Content Download in Vehicular Networks in Presence of Noisy Mobility Prediction

Bandwidth availability in the cellular backhaul is challenged by ever-increasing demand by mobile users. Vehicular users, in particular, are likely to retrieve large quantities of data, choking the cel- lular infrastructure along major thoroughfares and in urban areas. It is envisioned that alternative roadside network connectivity can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of vehicular networks in this task, considering that roadside units can exploit mobility prediction to decide which data they should fetch from the Internet and to schedule transmissions to vehicles. Rather than adopting a specific prediction scheme, we propose a fog-of-war model that allows us to express and account for different degrees of prediction accuracy in a simple, yet effective, manner. We show that our fog-of-war model can closely reproduce the prediction accuracy of Markovian techniques. We then provide a probabilistic graph-based representation of the system that includes the prediction information and lets us optimize content prefetching and transmission scheduling. Analytical and simulation results show that our approach to content downloading through vehicular networks can achieve a 70% offload of the cellular networ

Wi-Fi Offload: Tragedy of the Commons or Land of Milk and Honey?

Fueled by its recent success in provisioning on-site wireless Internet access, Wi-Fi is currently perceived as the best positioned technology for pervasive mobile macro network offloading. However, the broad transitions of multiple collocated operators towards this new paradigm may result in fierce competition for the common unlicensed spectrum at hand. In this light, our paper game-theoretically dissects market convergence scenarios by assessing the competition between providers in terms of network performance, capacity constraints, cost reductions, and revenue prospects. We will closely compare the prospects and strategic positioning of fixed line operators offering Wi-Fi services with respect to competing mobile network operators utilizing unlicensed spectrum. Our results highlight important dependencies upon inter-operator collaboration models, and more importantly, upon the ratio between backhaul and Wi-Fi access bit-rates. Furthermore, our investigation of medium- to long-term convergence scenarios indicates that a rethinking of control measures targeting the large-scale monetization of unlicensed spectrum may be required, as otherwise the used free bands may become subject to tragedy-of-commons type of problems.Comment: Workshop on Spectrum Sharing Strategies for Wireless Broadband Services, IEEE PIMRC'13, to appear 201

On Factors Affecting the Usage and Adoption of a Nation-wide TV Streaming Service

Using nine months of access logs comprising 1.9 Billion sessions to BBC iPlayer, we survey the UK ISP ecosystem to understand the factors affecting adoption and usage of a high bandwidth TV streaming application across different providers. We find evidence that connection speeds are important and that external events can have a huge impact for live TV usage. Then, through a temporal analysis of the access logs, we demonstrate that data usage caps imposed by mobile ISPs significantly affect usage patterns, and look for solutions. We show that product bundle discounts with a related fixed-line ISP, a strategy already employed by some mobile providers, can better support user needs and capture a bigger share of accesses. We observe that users regularly split their sessions between mobile and fixed-line connections, suggesting a straightforward strategy for offloading by speculatively pre-fetching content from a fixed-line ISP before access on mobile devices.Comment: In Proceedings of IEEE INFOCOM 201

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

We study the off-line efficient mobile edge computing (EMEC) problem for a joint computing to process a task both locally and remotely with the objective of minimizing the finishing time. When computing remotely, the time will include the communication and computing time. We first describe the time model, formulate EMEC, prove NP-completeness of EMEC, and show the lower bound. We then provide an integer linear programming (ILP) based algorithm to achieve the optimal solution and give results for small-scale cases. A fully polynomial-time approximation scheme (FPTAS), named Approximation Partition (AP), is provided through converting ILP to the subset sum problem. Numerical results show that both the total data length and the movement have great impact on the time for mobile edge computing. Numerical results also demonstrate that our AP algorithm obtain the finishing time, which is close to the optimal solution