Integrated End-to-End Quality Control

for Delivering Interactive/Live/Pre-stored Videos over Networks

Abstract

With improved capabilities of network infrastructures and video compression techniques, rich multimedia applications such as video conferencing, live broadcasting news and streaming contents are dramatically boosted to clients. Many equipment venders provide robust core/access routers and L2/L3 switches for handling the huge multimedia traffics. Yet Variable-Bit-Rate (VBR) compressed video exhibits significant rate variability, even when the rate is computed over time intervals as large as several minutes. This burstiness complicates the design of efficient real-time storage, retrieval, and transport mechanisms capable of achieving high resource utilization. Therefore, a mechanism that can effectively reduce the rate variability of video data and timely send the accurate traffic information of video stream to network facilities is very important for effective resource management. On the other hand, the Resynchronization Marking (RM) scheme proposed in most video coding standards such as MPEG-4 is generally used to improve the error resilience capability of video data in the wireless network. However, if the RM scheme at the application layer is executed independently, the effective throughput of WLAN may be decreased. This phenomenon is particularly true if the multi-layer header overhead increases or the wireless channel condition changes obviously.

Therefore, this dissertation proposes an Integrated End-to-End Video Delivery System (IE-VDS) that can provide robust and efficient delivery quality for pre-stored, live broadcasting and real-time videos over networks. IE-VDS includes four major parts, the Optimal Packet size Determination Mechanism (OPDM), theρ-domain Long-Span Predictor (ρ-LSP), the Intelligent ONline Traffic Smoothing mechanism (ON-ITS), and the Deterministic OFFline Traffic Smoothing mechanism (OFF-DTS). OPDM proposes a simple yet robust closed-form that can determine accurately and timely the optimal video payload length at the video sender based on the current wireless channel condition. Whenever the optimal payload length that can maximize the WLAN throughput is determined, the RM scheme adopts the optimal payload length to packetize the video data. For executing efficient online traffic smoothing, ρ-LSP predicts the bitrate of future frames with high accuracy by utilizing the linear relationship between the cumulated frame bitrates and the total number of nonzero motion vectors and nonzero quantized coefficients of encoded frames. ON-ITS then proposes an intelligent online traffic smoothing mechanism that integrates the proposed adaptive window size method withρ-LSP to improve the flexibility and performance of online smoothing for live broadcasting and real-time video applications. On the other hand, for delivering pre-stored video contents with deterministic QoS guarantees, OFF-DTS integrates proper traffic smoothing operations with the traditional deterministic traffic modeling scheme to reduce the playback buffer demand and data rate variation. Herein the limitation of playback buffer space, the network delay jitter, the processing load of resource management, and the QoS guarantee are considered in the proposed mechanism.

Analytical and simulation results verify the accuracy and effectiveness of the proposed closed-form in OPDM. The contribution effectively improves the WLAN throughput and enhances the error resilience effect of scalable video data simultaneously. From simulation results, ON-ITS effectively reduces the transmission rate variation and the peak rate demand of real-time and live video services while remaining a low playback delay. Moreover, OFF-DTS effectively satisfies the situation of insufficient playback buffer space in mobile clients while still maintaining the advantages of deterministic services for pre-stored videos.

Key words ¾ resource management, traffic predicting, traffic smoothing, packetization, wireless network