Beyond multimedia adaptation: Quality of experience-aware multi-sensorial media delivery

Multiple sensorial media (mulsemedia) combines multiple media elements which engage three or more of human senses, and as most other media content, requires support for delivery over the existing networks. This paper proposes an adaptive mulsemedia framework (ADAMS) for delivering scalable video and sensorial data to users. Unlike existing two-dimensional joint source-channel adaptation solutions for video streaming, the ADAMS framework includes three joint adaptation dimensions: video source, sensorial source, and network optimization. Using an MPEG-7 description scheme, ADAMS recommends the integration of multiple sensorial effects (i.e., haptic, olfaction, air motion, etc.) as metadata into multimedia streams. ADAMS design includes both coarse- and fine-grained adaptation modules on the server side: mulsemedia flow adaptation and packet priority scheduling. Feedback from subjective quality evaluation and network conditions is used to develop the two modules. Subjective evaluation investigated users' enjoyment levels when exposed to mulsemedia and multimedia sequences, respectively and to study users' preference levels of some sensorial effects in the context of mulsemedia sequences with video components at different quality levels. Results of the subjective study inform guidelines for an adaptive strategy that selects the optimal combination for video segments and sensorial data for a given bandwidth constraint and user requirement. User perceptual tests show how ADAMS outperforms existing multimedia delivery solutions in terms of both user perceived quality and user enjoyment during adaptive streaming of various mulsemedia content. In doing so, it highlights the case for tailored, adaptive mulsemedia delivery over traditional multimedia adaptive transport mechanisms

Congestion Control using FEC for Conversational Multimedia Communication

In this paper, we propose a new rate control algorithm for conversational multimedia flows. In our approach, along with Real-time Transport Protocol (RTP) media packets, we propose sending redundant packets to probe for available bandwidth. These redundant packets are Forward Error Correction (FEC) encoded RTP packets. A straightforward interpretation is that if no losses occur, the sender can increase the sending rate to include the FEC bit rate, and in the case of losses due to congestion the redundant packets help in recovering the lost packets. We also show that by varying the FEC bit rate, the sender is able to conservatively or aggressively probe for available bandwidth. We evaluate our FEC-based Rate Adaptation (FBRA) algorithm in a network simulator and in the real-world and compare it to other congestion control algorithms

Congestion Control for Network-Aware Telehaptic Communication

Telehaptic applications involve delay-sensitive multimedia communication between remote locations with distinct Quality of Service (QoS) requirements for different media components. These QoS constraints pose a variety of challenges, especially when the communication occurs over a shared network, with unknown and time-varying cross-traffic. In this work, we propose a transport layer congestion control protocol for telehaptic applications operating over shared networks, termed as dynamic packetization module (DPM). DPM is a lossless, network-aware protocol which tunes the telehaptic packetization rate based on the level of congestion in the network. To monitor the network congestion, we devise a novel network feedback module, which communicates the end-to-end delays encountered by the telehaptic packets to the respective transmitters with negligible overhead. Via extensive simulations, we show that DPM meets the QoS requirements of telehaptic applications over a wide range of network cross-traffic conditions. We also report qualitative results of a real-time telepottery experiment with several human subjects, which reveal that DPM preserves the quality of telehaptic activity even under heavily congested network scenarios. Finally, we compare the performance of DPM with several previously proposed telehaptic communication protocols and demonstrate that DPM outperforms these protocols.Comment: 25 pages, 19 figure

Towards a sender-based TCP friendly rate control (TFRC) protocol

Pervasive communications are increasingly sent over mobile devices and personal digital assistants. This trend is currently observed by mobile phone service providers which have measured a significant increase in multimedia traffic. To better carry multimedia traffic, the IETF standardized a new TCP Friendly Rate Control (TFRC) protocol. However, the current receiver-based TFRC design is not well suited to resource limited end systems. In this paper, we propose a scheme to shift resource allocation and computation to the sender. This sender-based approach led us to develop a new algorithm for loss notification and loss-rate computation. We detail the complete implementation of a user-level prototype and demonstrate the gain obtained in terms of memory requirements and CPU processing compared to the current design. We also evaluate the performance obtained in terms of throughput smoothness and fairness with TCP and we note this shifting solves security issues raised by classical TFRC implementations

CloudJet4BigData: Streamlining Big Data via an Accelerated Socket Interface

Big data needs to feed users with fresh processing results and cloud platforms can be used to speed up big data applications. This paper describes a new data communication protocol (CloudJet) for long distance and large volume big data accessing operations to alleviate the large latencies encountered in sharing big data resources in the clouds. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX) and thereby can accelerate any POSIX-compatible applications across IP based networks. It was demonstrated that CloudJet accelerates typical big data applications such as very large database (VLDB), data mining, media streaming and office applications by up to tenfold in real-world tests