A Quality-Driven Decision Engine for Live Video Transmission under Service-Oriented Architecture

DALEI WU, SONG CI, HAIYAN LUO, UNIVERSITY OF NEBRASKA-LINCOLNHAOHONG WANG, MARVELL SEMICONDUCTORS

AGGELOS KATSAGGELOS, NORTHWESTERN UNIVERSITY

IEEE Wireless Communications, Aug. 2009

Outline

Introduction Service-oriented Architecture(SOA) Current real-time video transmission

Proposed SOA system Case study Experiment result Conclution

Introduction – Service-Oriented Architecture

SOA has been regarded as a promising distributed network management method in large-scale heterogeneous communications networks

Entire video communication system can be decomposed into many different services provided by one or more service providers.

Introduction

Two types of live video applications: Video streaming application (Youtube)

pre-encoded and packetized at the same server Cannot adapted to changes such as network

congestions. Interactive video application

(videoconferencing) videos are coded on-the-fly source content and network conditions are

jointly considered to determine the optimal encoding modes

Prosoped SOA system

Prosoped SOA system Decision engine can retrieve the user profile

information and services from the broker network, optimize the service configuration, and implement different capacities of applications.

User perceived video quality

Available services

Different capacity of apps

Media Signal Processing Service

Based on different user profiles and available network resources, decision engine selects different media signal processing algorithms (services) to deal with user requests. Extracting the ROI Downsampling Filtering the high-frequency component Encoding or transcoding a video sequence Dropping the current frame

Performance Evaluation Service

Network-centric metrics such as throughput, delay fail to provide an efficient and accurate evaluation Different importance of video bitstream Continuous and smooth playback Error resilience and concealment

Application-centric metrics such as expected end-to-end video quality are the most straightforward and reasonable.

Calculation of video quality is based on some predefined rate-distortion function or model.

Network Service

Path selection Multiple paths in a multihop network

that may provide different levels of reliability

Decision engine integrate some existing routing protocol, such as optimal link state routing (OLSR), into a workflow to find the optimal transmission path.

Network service

Resourse allocation Multimedia data of a given video stream have

different levels of importance to the user-perceived video quality

Various resourse allocation and scheduling approaches have been developed. Such as time slot/bandwidth allocation , packet ordering, and retransmission.

The decision engine needs to choose an appoach such that the user-perceived video quality is maximized while the utilization enhanced.

Case study

An SOA-based live video communication sysytem

1. N-frame video sequence C ={g1, …, gN}. Each video frame can be divided into a foreground and a background. Foreground part being the ROI.

2. Wireless network model as a DAG G(V, E) with node set V and edge set E.

3. packet k over G delay deadline is associated with frame decoding deadline Tmax.

Case study(cont.)

4. Always checks the total delay of packet k at node v. If exceeds Tmax, packet k should be discarded.

5. Use pixel recursive algorithm(ROPE) to performance evaluate, estimating the expected distortion.The contributions of foreground and background distortion to the user-perceived video can be weighted by λk .

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Case study(cont.)

6. The scheduling service Φk for packet k is based on the video quality evaluation result.Priority scheduling approach first scheduled the foreground packet for transmission.

7. The maximum number of retransmissions Πk (v,u) for packet k over link (v,u) is jointly determined by the packet delay constraint Tmax and the total delay

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Case study

Each packet k generated by the media signal processing service and transmitted by the network is characterized by: The source coding service Sk

The transmission path selection service Pk

The scheduling service Φk

The packet delay deadline Tmax

The quality impact factor λk

Object function

Expected distortion for packet k can be written as E[Dk] = Qk( Sk, Pk, Φk, Tmax, λk)

Object function for decision engine

V is the generated workflow by decision engine for end user.

Experimental Result

Identification of the ROI is performed by the following stages background subtraction split-and-merge morphological operations.

Experimental Result

Simulation parameters H.264/AVC JM 12.2 Video Clip: “Mother and Daughter.” 30-node network deployed over a 1000 m ×

1000m Source and destination are chosen randomly Transmission range: 150 m Generate 50 topologies and run 50

computations to obtain the average. Packet delay deadline Tmax: 0.033s

Experimental Result

Two network-centric routing service:PLR-based: packet loss rate as routing metricDelay-based: packet delay as routing metric

Experimental result

Without priority scheduling: foreground and background are the sameWith priority scheduling: foreground has a 4.5 dB PSNR better than whole video without IRI 9.5 dB PSNR better than background

Experimental result

(a)Original(b)Using content analysis and priority scheduling(c) Without using content analysis and priority

scheduling

Conclusion Traditional multimedia communication systems

are lacking the flexibility of end-to-end QoS for various multimedia applications, especially for live video applications.

A quality-driven decision engine for real-time video transmissions based on SOA jointly considered and optimized various kinds of data processing services by the decision engine.

Experimental results show that the proposed quality-driven service-oriented decision engine can provide better end-user experience.