Q-AIMD: A Congestion Aware Video Quality Control Mechanism

Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called Q-AIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, Q-AIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them

Greediness control algorithm for multimedia streaming in wireless local area networks

This work investigates the interaction between the application and transport layers while streaming multimedia in a residential Wireless Local Area Network (WLAN). Inconsistencies have been identified that can have a severe impact on the Quality of Experience (QoE) experienced by end users. This problem arises as a result of the streaming processes reliance on rate adaptation engines based on congestion avoidance mechanisms, that try to obtain as much bandwidth as possible from the limited network resources. These upper transport layer mechanisms have no knowledge of the media which they are carrying and as a result treat all traffic equally. This lack of knowledge of the media carried and the characteristics of the target devices results in fair bandwidth distribution at the transport layer but creates unfairness at the application layer. This unfairness mostly affects user perceived quality when streaming high quality multimedia. Essentially, bandwidth that is distributed fairly between competing video streams at the transport layer results in unfair application layer video quality distribution. Therefore, there is a need to allow application layer streaming solutions, tune the aggressiveness of transport layer congestion control mechanisms, in order to create application layer QoE fairness between competing media streams, by taking their device characteristics into account. This thesis proposes the Greediness Control Algorithm (GCA), an upper transport layer mechanism that eliminates quality inconsistencies caused by rate / congestion control mechanisms while streaming multimedia in wireless networks. GCA extends an existing solution (i.e. TCP Friendly Rate Control (TFRC)) by introducing two parameters that allow the streaming application to tune the aggressiveness of the rate estimation and as a result, introduce fair distribution of quality at the application layer. The thesis shows that this rate adaptation technique, combined with a scalable video format allows increased overall system QoE. Extensive simulation analysis demonstrate that this form of rate adaptation increases the overall user QoE achieved via a number of devices operating within the same home WLAN

Xstream-x264: Real-time H.264 streaming with cross-layer integration

We present Xstream-x264: a real-time cross-layer video streaming technique implemented within a well known open-source H.264 video encoder tool x264. Xstream-x264 uses the transport protocol provided indication of the available data rate for corresponding adjustments in the video encoder.We discuss the design, implementation and the quality evaluation methodology utilised with our tool.We demonstrate via experimental results that the streaming video quality greatly improves with the presented cross-layer approach both in terms of lost frame count and the objective video quality metrics Peak Signal to Noise Ratio (PSNR)

Measuring TFRC & SCTP Performance Over AODV In MANET

This study focuses on TCP-Friendly rate control (TFRC), which is defined by IETF in RFC 5348 as and Stream Control Transmission Protocol (SCTP), which is defined by IETF in RFC 4960 as a new transport protocol. TFRC feature as fairness has attracted real time application and SCTP features also as multi-homing and multi-streaming, has attracted multimedia applications to use it as their transport protocol instead of TCP and UDP. However, the challenge faced by TFRC that is using additive increase to adjust the sending rate during periods with no congestion. This leads to short term congestion that can degrade the quality of voice applications. SCTP faced the challenge in a best-effort network. In this study, a comprehensive performance evaluation between TFRC and SCTP has been carried out. The objectives of this research are to measure the performance of both TFRC and SCTP in MANET in terms of throughput, delay and packet loss that has TFRC and SCTP with UDP traffic some times and some experiments without UDP and the nodes is in mobility positions. All experiments conducted in this research were obtained through network simulation tools using NS-2. It is expected that this study is useful for researchers in improving both TFRC and SCTP