Quality of experience (QoE) driven adaptation scheme for?voice/video over IP

Network quality of service (NQoS) of IP networks is unpredictable and impacts the quality of networked multimedia services. Adaptive voice and video schemes are therefore vital for the provision of voice over IP (VoIP) services for optimised quality of experience (QoE). Traditional adaptation schemes based on NQoS do not take perceived quality into consideration even though the user is the best judge of quality. Additionally, uncertainties inherent in NQoS parameter measurements make the design of adaptation schemes difficult and their performance suboptimal. This paper presents a QoE-driven adaptation scheme for voice and video over IP to solve the optimisation problem to provide optimal QoE for networked voice and video applications. The adaptive VoIP architecture was implemented and tested both in NS2 and in an Open IMS Core network to allow extensive simulation and test-bed evaluation. Results show that the scheme was optimally responsive to available network bandwidth and congestion for both voice and video and optimised delivered QoE for different network conditions, and is friendly to TCP traffic.     

Fuzzy logic congestion control for broadband wireless IPTV

All-IP broadband networks are being created with multimedia bandwidth requirements in mind. A unicast IPTV service forming a pipe or sub-channel on the converged network may need to negotiate a broadband wireless link. Where there is a need for multiple variable bit rate video streams to share the same pipe a problem of link utilisation arises [1], requiring congestion control at the server bank. Conventional controllers such as TCP-friendly rate control (TFRC) [2] and TCP emulation at receivers (TEAR) [3], originating in a TCP-dominated internet, will stream video up to the capacity of the pipe, but reacting to feedback may overestimate the capacity, resulting in packet loss, which leads to reduced video quality. In this Letter, fuzzy logic control (FLC) is shown to outperform conventional control in such a network by changing the quantisation parameter for live video or through a bit rate transcoder for pre-encoded video. Moreover, compared to prior use of traditional (type-1) fuzzy logic for similar purposes [4], interval type-2 (IT2) FLC has been employed, as this is robust to feedback measurement uncertainties.     

Type-2 fuzzy logic control of PQoS driven adaptive VoIP scheme

Adaptive VoIP schemes have potentially suboptimal performance owing to imprecision in the metrics used to infer network state. An interval Type-2 fuzzy logic controlled scheme for VoIP services is presented. It infers network state from average delivered perceived quality of service and its degradation due to network congestion and updates an AMR codec mode to match voice quality to available network bandwidth. Tests showed that the scheme maximised delivered voice quality and outperformed an existing adaptive scheme. The scheme achieves robust performance in the presence of input imprecision and can be implemented in VoIP terminals, and the fuzzy rule base is easy to understand and change by non-experts because of its similarity to the human decision-making process.     

Efficient packetisation scheme for bluetooth video transmission

The Bluetooth wireless link is likely to be the last hop in the delivery of an encoded streamed video clip. It is shown that it is preferable to optimally map arriving IP packets onto Bluetooth packets than to preserve the stream's internal synchronisation structure. Video quality improves and latency reduces, even when there is cross traffic on the piconet     

Smoothing transcoded MPEG-1 video streams for Internet transmission

An efficient smoothing scheme for the real-time transmission of MPEG-1 transcoded video over 'best-effort' IP networks is presented. The scheme uses intelligent partitioning and multiplexing of the packetised bit stream. Bit-rate smoothing is achieved by partitioning packets according to their picture type (I, P or B). Subsequently, the partitioned packets are multiplexed in such a way that each packet from an anchor (I and P) picture is followed by two packets from B-pictures. The proposed scheme smooths the bit rate of the encoded video, making it more suitable for adaptive video streaming applications. In such applications, the transmission bit rate is varied so as to adapt to the available network bandwidth. The scheme reorganises the transmission order of the packets, spreading the less important packets from the B-pictures into the more important packets from the anchor pictures. This indicates a reduction in the likelihood of losing more important anchor packets as the bandwidth bottleneck increases, implying an improvement in the quality of transmitted video. A variety of simulations results are presented to demonstrate these points.     

Rate-adaptive video streaming through packet dispersion feedback

The anticipated growth of IPTV makes selection of suitable congestion controllers for video-stream traffic of vital concern. Measurements of packet dispersion at the receiver provide a graded way of estimating congestion, which is particularly suited to video as it does not rely on packet loss. A closed-loop congestion controller, which dynamically adapts the bitstream output of a transcoder or video encoder to a rate less likely to lead to packet loss, is presented. The video congestion controller is based on fuzzy logic with packet dispersion and its rate of change forming the inputs. Compared with TCP emulators such as TCP-friendly rate control (TFRC) and rate adaptation protocol (RAP), which rely on packet loss for real-time congestion control, the fuzzy-logic trained system?s sending rate is significantly smoother when multiple video-bearing sources share a tight link. Using a packet dispersion method similarly results in a fairer allocation of bandwidth than TFRC and RAP. These gains for video traffic are primarily because of better estimation of network congestion through packet dispersion but also result from accurate interpretation by the fuzzy-logic controller.