Fine grained content-based adaptation mechanism for providing high end-user quality of experience with adaptive hypermedia systems

Abstract

New communication technologies can enable Web users to access personalised information "anytime, anywhere". However, the network environments allowing this "anytime, anywhere" access may have widely varying performance characteristics such as bandwidth, level of congestion, mobility support, and cost of transmission. It is unrealistic to expect that the quality of delivery of the same content can be maintained in this variable environment, but rather an effort must be made to fit the content served to the current delivery conditions, thus ensuring high Quality of Experience (QoE) to the users. This paper introduces an end-user QoE-aware adaptive hypermedia framework that extends the adaptation functionality of adaptive hypermedia systems with a fine-grained content-based adaptation mechanism. The proposed mechanism attempts to take into account multiple factors affecting QoE in relation to the delivery of Web content. Various simulation tests investigate the performance improvements provided by this mechanism, in a home-like, low bit rate operational environment, in terms of access time per page, aggregate access time per browsing session and quantity of transmitted information.

A novel bandwidth estimation algorithm for IEEE 802.11 TCP data transmissions

“…The key assumption of Chatzimisios's model is that the transmission loss probability (P DCF ) of a transmitted packet is constant and independent of the number of the collisions or errors occurred in the past. The probability P DCF is given by (2), where n indicates the number of contending stations, BER is the bit error rate, L is the packet size, H is the packet header, and τ denotes the probability that a station transmits a packet in a randomly chosen slot time. The probability τ is given by (3), where W represents the initial contention window size and m means retry limit.…”

“…The probability τ is given by (3), where W represents the initial contention window size and m means retry limit. Chatzimisios's model has proved that there is a unique solution for (2) and (3).…”

“…Bandwidth estimation techniques have proved to be significant to improve the Quality of Service (QoS) for many multimedia applications [1] [2] [3] [4]. Most of these techniques are proposed to provide accurate bandwidth estimations for wired networks, however, bandwidth estimation for wireless networks is more challenging due to the wireless channel characteristics such as Bit Error Rate (BER), fading, interference, contention, retry limit, etc.…”

Cultural Adaption of Hypermedia

“…Two types of nodes apply to the design of adaptive hypermedia (Brusilovsky, 2001;Muntean, 2005): navigational nodes and content-comprising nodes. Usually, websites are mixtures of different types of media (text, flash, documents, etc.).…”

Predicting file downloading time in cellular network: Large-Scale analysis of machine learning approaches

“…Yet, content providers should take some key decisions at the beginning of the delivery. For instance, most web sites have different versions of their pages, with a variable number of elements and different versions, e.g., pictures with different resolutions [1,2]. The decision of which version to deliver must be taken at the very beginning of the connection, even though no past observation of the network path performance is available.…”