220 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 21, NO. 2, FEBRUARY 2011
A Collaborative Transcoding Strategy for Live Broadcasting overPeer-to-Peer IPTV Networks
Jui-Chieh Wu, Polly Huang, Jason J. Yao, and Homer H. Chen, Fellow, IEEE
AbstractReal-time video transcoding that is often needed forrobust video broadcasting over heterogeneous networks is notsupported in most existing devices. To address this problem,we propose a collaborative strategy that leverages the peeringarchitecture of peer-to-peer Internet protocol television networksand makes the computational resources of peers sharable. Thevideo transcoding task is distributed among the peers and com-pleted collaboratively. A prototype of the live video broadcastingsystem is evaluated over a 100-node testbed on the PlanetLab.The experimental results show that the proposed strategy workseffectively even when the majority of the peers have limitedcomputational resource and bandwidth.
Index TermsIPTV, live broadcasting, multiple descriptioncoding, P2P network, video streaming.
V IDEO CONTENT distribution over peer-to-peer (P2P)networks has evolved and become an everyday norm .The evolution will continue as the deployment of broadbandwireless access, such as WiFi, 3G, and WiMAX, accelerates and as more people create and share contents of their ownover the Internet. The problem we are concerned with in thisletter is related to the support of live video broadcasting overa P2P Internet protocol television (IPTV) network.
Multiple description coding (MDC) ,  and layeredvideo coding (LVC)  techniques have been developedto enhance the quality of service for networks that are het-erogeneous in nature. However, most multimedia devices onlysupport popular coding standards such as MPEG-4 ,  as
Manuscript received September 29, 2008; revised April 27, 2009 andNovember 29, 2009; accepted August 13, 2010. Date of publication Jan-uary 13, 2011; date of current version March 2, 2011. This work wassupported in part by the grants from the National Science Council of Taiwan,under Contracts NSC 95-2219-E-002-012, NSC 94-2220-E-002-027, andNSC 95-2219-E-002-015. This paper was recommended by Associate EditorM. Comer.
J.-C. Wu is with the Graduate Institute of Computer Science and InformationEngineering, National Taiwan University, Taipei 10617, Taiwan (e-mail:email@example.com).
P. Huang and J. J. Yao are with the Department of Electrical Engi-neering and Graduate Institute of Communication Engineering, NationalTaiwan University, Taipei 10617, Taiwan (e-mail: firstname.lastname@example.org;email@example.com).
H. H. Chen is with the Department of Electrical Engineering, Graduate In-stitute of Communication Engineering, and Graduate Institute of Networkingand Multimedia, National Taiwan University, Taipei 10617, Taiwan (e-mail:firstname.lastname@example.org).
Digital Object Identifier 10.1109/TCSVT.2011.2105571
the native compression format and cannot perform MDC orLVC in real time.
To break the computational bottleneck, we leverage theP2P streaming architecture of an IPTV system and makethe computational resource, in addition to bandwidth, ofthe peers sharable. Under this strategy, the native streamgenerated by the source peer is divided into small segmentsand assigned to peers that have the computational resource toshare the transcoding workload. That is, the transcoding taskis distributed among peers and completed collaboratively.
The notion of P2P networking for resource sharing hasbeen adopted in various systems . However, ourapproach is different in that the source peer does not collectthe distributed results from the peers and that the transcod-ing is carried out in a pure P2P fashion without centralgoverning.
II. System Overview
The collaborative video transcoding strategy is built upon aP2P IPTV system  as the baseline, which incorporates pull-based content delivery (swarming) and layered encoding tostream media to heterogeneous viewers. The system architec-ture is shown in Fig. 1. The partnership formation componentmaintains the partner relationship between the peers, and thepeer information exchange component maintains a buffer mapthat records the data availability. The exchange period of thebuffer maps is referred to as the swarming cycle. Based onthe buffer map, the segment scheduling module searches thevideo segments that can arrive before the display time and aredownloadable within the available bandwidth.
The baseline system is extended to incorporate thecollaborative transcoding strategy for live video broadcasting.A peer passes a transcoding task to its downstream peers ifit cannot handle the task, which involves decoding a segmentin the native compression format and re-encoding it into thetarget format.
The source peer generates a native compressed video streamand broadcasts it through the P2P network. The receivers areclassified into transcoding peers (those which transcode thereceived segments) and transporting peers (those which onlytransport the received segments without transcoding them).After going through one or more overlay hops in the P2P
1051-8215/$26.00 c 2011 IEEE
WU et al.: A COLLABORATIVE TRANSCODING STRATEGY FOR LIVE BROADCASTING OVER PEER-TO-PEER IPTV NETWORKS 221
Fig. 1. P2P IPTV system architecture with collaborative transcoding for livevideo broadcasting. The highlighted area represents the baseline system.
network, a segment in the native format is converted to thetarget format.
There are three requirements. First, a peer should collectthe information about the available computing power of otherpeers. Second, to avoid assigning a transcoding task too manytimes, a peer should collect information of the transcodingstatus of a segment. Third, based on the collected information,a peer should assign a transcoding task (defined in Section IV)to each downstream peer, and each downstream peer shouldbe able to determine if a task is acceptable and, if yes, itspriority.
Consequently, the baseline system is extended to equipwith: 1) capability estimation; 2) exchange of capability andtranscoding status information; and 3) job scheduling. Thecapability estimation component periodically estimates theavailable computing power of a peer. The extended peerinformation exchange component manages the exchange ofinformation with other peers about the computational capabil-ity of a peer and the transcoding status of a video segment.The job scheduling component, which is integrated with thesegment scheduling component of the baseline system, deter-mines for a partner peer which segments should be sent to therequesting peer and which requesting peers can transcode thesegments. For a requesting peer, this component determineswhich segments to download.
III. Computational Capability Estimation
Computational capability estimation is a challenging issuebecause the available computational resource varies fromdevice to device and depends on factors such as the centralprocessing unit (CPU) clock speed, the random access memory(RAM) size, the current CPU load, the transcoding scheme,and the video characteristics. For each specific CPU clockspeed and RAM size, it is possible to determine and recordthe available computing power in a lookup table. However, it ispractically impossible to enumerate all possible combinations.
To have a robust and universal mechanism that is indepen-dent of codec, hardware platform, and operating system, weuse the average transcoding time ta of a 1-s video segment for
Fig. 2. Structure of a buffer map.
Fig. 3. Five phases of collaborative video transcoding and their values inbinary format.
computational capability estimation and update it whenever thepeer completes a transcoding task. The number of segments apeer can handle in one swarming cycle Cs is determined bydividing the swarming cycle ts by ta.
The capability estimate cannot be completely accurate sincethe available resource of a peer varies over time. Therefore,in the event that a peer is unable to finish all tasks within aswarming cycle due to an overestimate of its computationalcapability, the pending transcoding tasks must be subtractedfrom Cs in the next swarming cycle.
IV. Peer Information Exchange
In each swarming cycle, a peer requests the buffer mapfrom its partner peers. The request message also contains theavailable uplink bandwidth, the number of requesting peers,and the computational capability of the peer. The availableuplink bandwidth is estimated by using the transmitted mediadata as the probing packets to avoid traffic overload. Uponreceiving the request, a partner peer uses the informationcontained in the request message to determine the candidatesegment and responds to the request with a buffer map for jobscheduling.
The buffer map extended from the baseline system carriesadditional information about transcoding, as shown in Fig. 2.A buffer map consists of one 8-bit long ID and 64 contiguoussegment descriptors, each of which is 8-bit long and corre-sponds to a 1-s video segment. The last 4 bits of a segmentdescriptor indicate which MDC descriptions  are availableif it is an MDC segment, or the number of requesting peersthat receive the segment for transcoding if this segment is nottranscoded yet. The latter is needed for job scheduling.
The first 4 bits of the descriptor of a segment describe thetranscoding status of the segment, namely, initial (1), transport(2), candidate (3), progressing (4), and complete (5), as shownin Fig. 3. The first four phases mean that the segment is stillin the native compression format, whereas the last one meansthe segment is completely transcoded to the MDC format.
The transcoding status of a new se