Pietro Lungaro

ActiveCast - a network and user aware mobile content delivery system

Most of the currently deployed networks are typically dimensioned considering the “peak hour” traffic demand. Opportunistically utilizing these “excess” resources might be an effective way for improving utilization and lowering the “production” costs. This paper is proposing and evaluating a novel concept, called ActiveCast, and the corresponding architecture for a network and user behavior aware mobile content delivery system. When considering real traffic measurements in urban scenarios we showed that the concept improves the resource utilization and allows serving significantly more users in a pre-existing network. Even with moderate amounts of reliable context information, ActiveCast have been shown to drastically improving both user quality of experience perception and network efficiency, as compared to conventional on-demand content delivery schemes.

ContextShift: A Model for Efficient Delivery of Content in Mobile Networks

A context aware model for delivery of content in mobile networks is introduced and studied through simulation. The model is based on predictive knowledge of mobile user and mobile group behavior. The predictive behavior is related to consumption of mobile content on smart mobile terminals. The model proposes to change the time and the location of the delivery of predicted content consumption to optimize the wireless network utilization and to improve the user experience. Different content delivery strategies, considering pre-fetching at the user terminals, caching at the base stations and multi-cast wireless transmissions are proposed and investigated. The simulation results show substantial gains in the content delivery efficiency of cellular networks and improved user perceived quality for a number of realistic network operation regimes.

Energy Consumption Reduction via Context-Aware Mobile Video Pre-fetching

The arrival of smart phones and tablets, along with a flat rate mobile Internet pricing model have caused increasing adoption of mobile data services. According to recent studies, video has been the main driver of mobile data consumption, having a higher growth rate than any other mobile application. However, streaming a medium/high quality video files can be an issue in a mobile environment where available capacity needs to be shared among a large number of users. Additionally, the energy consumption in mobile devices increases proportionally with the duration of data transfers, which depend on the download data rates achievable by the device. In this respect, adoption of opportunistic content pre-fetching schemes that exploit times and locations with high data rates to deliver content before a user requests it, has the potential to reduce the energy consumption associated with content delivery and improve the users quality of experience, by allowing playback of pre-stored content with virtually no perceived interruptions or delays. This paper presents a family of opportunistic content pre-fetching schemes and compares their performance to standard on-demand access to content. By adopting a simulation approach on experimental data, collected with monitoring software installed in mobile terminals, we show that content pre-fetching can reduce energy consumption of the mobile devices by up to 30% when compared to the on demand download of the same file, with a time window of 1 hour needed to complete the content prepositioning.

A novel paradigm for context-aware content pre-fetching in mobile networks

The current content provision methods and associated pricing and business models are challenged by the traffic requirements anticipated for future “data intensive” services. In order to deliver substantially higher peak rates operators will need to deploy a much denser infrastructure and/or acquire more spectrum, thus significantly increasing their CAPEX and OPEX and reducing revenues. To improve the utilization of available network resources this paper presents ActiveCast, a disruptive content delivery paradigm that supports opportunistic content pre-fetching by introducing semantic and context awareness in the currently “agnostic” networking paradigm. The experimental investigations presented in the paper focus on mobile video provision and a content provider, integrated with Facebook and YouTube, has been developed and used to identify socially relevant content for a set of test users. Part of the studies presented in the paper aim at experimentally understanding the structure of the energy costs associated with pre-fetching and on defining a delivery strategy that allows controlling the amount of energy invested. A comparison between a centralized implementation, in which pre-fetching is coordinated by the mobile operators, and an Over-The-Top (OTT) implementation of ActiveCast are also presented. The results show that complementing the context information available at individual user terminals with traffic information, shared by mobile operators through the ActiveCast API, can substantially reduce the energy costs of content delivery, as compared with “on demand” video streaming. Additionally, opportunistically exploiting connections with WiFi APs can amplify the gains already achievable by prefetching on wide area networks.

An Experimental Framework to Investigate Context-Aware Schemes for Content Delivery

A novel experimental approach for investigating the performances of context-aware content delivery schemes is presented in this paper. An innovative testbed, capable of remotely controlling multiple terminals, injecting a wide range of traffic loads in real networks and monitoring different performance measures has been developed and utilized for quantifying both the energy costs and user perceived service quality associated with different context- aware content pre-fetching schemes. In the implementation proposed in this paper, the context information required for performing content pre-fetching is extracted and utilized by individual user terminals and does not require any support from mobile operators. The performances of pre-fetching are compared to those of an on-demand content delivery scheme, for both video streaming and file downloading services. The results shows that not only pre-fetching can increase user service appreciation by reducing the time needed to access the information, but it can also significantly lower the amount of energy consumed in user terminals for retrieving the content. Our experiments further indicate that in order to achieve these additional energy gains only limited content prediction capabilities are required, thus making pre-fetching a solid candidate for the provision of a wide range of content types and services in both wide and local area networks.

Eye-gaze Based Service Provision and QoE Optimization

This paper presents an innovative approach to the optimization of multimedia content delivery for future data intensive services, featuring an eye-gaze based paradigm for mobile video provision. The proposed solution uses real-time eyegaze information, collected by connected eye-trackers, to provide high resolution in the portion of a video frame corresponding to the central part of a user’s field of view and lower quality in the areas corresponding to the user’s peripheral vision. This approach brings new optimization opportunities, leading to significant reduction in terms of network and device resources needed to deliver high quality experience to the users. The initial results showed potential bandwidth savings on the order of 60%, while maintaining high levels of user perceived video quality. Relevant areas of application include head mounted displays for mobile virtual and augmented reality, where substantial resource consumption savings are anticipated due to their high screen resolutions. The paper also introduces and discusses few architectural embodiments as well as additional services enabled by this technology. The main focus is on utilizing the vast amount of eye-gaze information, accumulated during content provision, to build representative models for individual users’ attention and content consumption behaviors.

Towards QoE-aware adaptive video streaming

This paper describes a novel QoE-aware adaptive video streaming method that enhances the viewing experience on mobile devices and reduces cellular network bandwidth consumed by Dynamic Adaptive Streaming over HTTP (DASH) by considering perceptual video quality and data rate channel conditions in the bitrate adaptation process. By streaming an optimized video for the particular video quality and channel conditions to a mobile device, we can improve the worst video qualities caused by DASH streaming and reduce quality variations using fewer number of bits.

QoE aware video content adaptation and delivery

The explosion of traffic associated with video content poses significant challenges for mobile content provision. While, on the one hand, mobile video traffic surge is forecast-ed to require significant investments in bandwidth acquisition and infrastructure dimensioning and roll-out, on the other hand, users are not likely to be willing to pay significantly more than today. This increases the pressure to develop solutions capable of making the mobile provision of video more affordable without either affecting user experience or limiting usage. In this respect, this paper proposes a novel methodology for video content delivery which is based on a user video quality perception model. According to this scheme, the video quality of each scene in a movie is selected, from among a finite set of available qualities, with the purpose of reducing the overall bandwidth required to attain a given user experience level targeted by the system for each user and each video. This novel methodology also adopts a clustering approach to identify users with similar Quality of Experience (QoE) profiles and leverages this information for improving the accuracy of user perceived quality predictions. This approach has been validated through a crowd-sourced subjective test evaluation performed with real users using a novel method involving the Amazon Mechanical Turk platform. The results showed that the proposed method is capable of achieving a prediction accuracy in the order of ±0.5 MOS points. This approach can be effectively used to select the video qualities minimizing bandwidth costs while delivering predefined level of perceived quality to the end users.

Application-centric content delivery schemes for future wireless networks

The apps paradigm is rapidly changing the way in which content is transferred and consumed in mobile networks. Currently, the traffic loads generated by apps are mostly provided as Over-The-Top (OTT) services, essentially transparent to the cellular network operators, while some of the content is delivered and updated in user terminals through “background transmissions”, without user intervention. With increasing traffic volumes, associated to richer content and more advanced devices, the apps paradigm might create severe system inefficiencies. In this paper we explore a number of apps based content delivery methods and investigate their performances on multiple relevant dimensions, such as: terminal energy consumption, time for accessing in-apps content and their impact on the user experience associated with other mobile data services. The proposed methods include opportunistic content pre-fetching and are characterized by different degrees of context-awareness. One approach considers only context information at the individual user terminals, while another one includes network context information and assumes that content distribution coordination is performed by the network operators. The results show that when one is increasing the numbers of apps, whose content needs to be simultaneously maintained updated, only the operator-driven approach can be feasible, mainly from an energetic perspective. However, for a small number of maintained apps, pre-fetching schemes are superior to standard “on-demand” content delivery solutions, suggesting that pre-fetching should be limited only to the subset of terminal apps with higher user access probabilities.

Distributed and Centralized Mobility-Aware Caching Schemes for Collaborative Service Provision in User-Deployed Networks

Opening up to public access, a collection of user- deployed APs has the potential to reduce service costs and improve user service perception, especially for delay tolerant services. However, since the individual APs composing such a network are typically owned and operated by different users, the achievable performances are likely to be limited by the lack of coordination. By adopting an Inter AP communication Protocol (IAPP) that allows APs to collaborate in exchanging messages about the information requested by potentially incoming users, the APs are capable to start caching information, even before the users reach their communication ranges. In a multi-user scenario, in which the backbone capacity limits the performances of the APs, a set of distributed backbone resource management schemes, differing in their degrees of collaboration between APs and their mobility-awareness, are proposed and evaluated in respect to both a centralized caching server solution and a non cooperative scheme. The results show that, at the cost of increased memory requirements, collaborative caching brings significant improvements to both backbone capacity utilization and average user rates, as compared to the non cooperating scheme. At the same time, we show that for a centralized server solution to outperform our collaborative caching scheme, the central server needs to have a backbone capacity comparable to sum of the capacities of the individual APs deployed in the service area.