On Large-Scale Peer-to-Peer Streaming Systems with Network Coding Chen Feng, Baochun Li Dept. of Electrical and Computer Engineering University of Toronto Presentation by: Shabnam Mirshokraie ACM Multimedia 2008

Peer to Peer Streaming Challenges How do we maintain a high playback quality at all participating peers? How do we improve user experience with the shortest initial buffering delay? How do we minimize server bandwidth costs? How do we design a system that is resilient to peer dynamics?

Related Work On achieving minimum delay A centralized algorithm [Y. Liu ACM Multimedia 07] On achieving maximum streaming rate A decentralized algorithm [Massoulie et al. INFOCOM 07] On achieving near optimal steaming rate and delay Several decentralized algorithms [Bonald et al. SIGMETRICS 08]

Related Work (Cont.) None of them is actually implemented by system designers No performance guarantees on resilience Centralized algorithms go nowhere Complexity and overhead issues

Practical Implementation Mesh based pull streaming strategies A live stream is divided into segments Segments arrive at a peer in roughly sequential order

Mesh Based Pull Streaming Cool Streaming (INFOCOM 05)

Mesh Based Pull Streaming (Cont.) Advantages Simplicity of implementation Better resilience to peer dynamics Disadvantages Significant overhead of requests and buffer availability exchanges Longer initial buffering delays

Designing a Good P2P Streaming System Simple to implement Low protocol overhead With theoretical guarantees on Smooth playback Short initial buffering delay Low server bandwidth costs Resilience to peer dynamics

Streaming With Network Coding

Random Push on a Random Mesh

Random Push on a Random Mesh (Cont.) Traditional pull based strategies Large buffer map size Frequently buffer map exchanges Explicit requests messages Random push with random Network Coding Smaller buffer map Less frequent buffer map exchanges No need for explicit request messages

Synchronized Playback Synchronized playback buffers on all peers All peers play the same segment at approximately the same time Playback buffers overlap as much as possible The new peer skips a few segments Receiving segments that are D seconds after the current point of playback The duration of D seconds corresponds to the initial buffering delay

Performance Analysis of Coding Quantitative answers to the following questions What are the sufficient conditions for Coding to achieve good overall performance? How far from optimality is the performance of Coding? Exploring the performance gap between Coding and optimal streaming scheme Motivation for more elaborated designs

System Model and Notations

System Model and Notations (Cont.) Flash crowd scenario most of the peers join the system in a short time period Highly dynamic scenario peers join and leave the system in a highly volatile Fashion (peer churn)

Flash Crowd Scenarios

Flash Crowd Scenarios (Cont.)

Theorem 1 establishes sufficient conditions on smooth playback Heterogeneity in upload capacity in not an issue in Coding High bandwidth utilization

Flash Crowd Scenarios (Cont.)

Apply Theorem 1 to understand the gap between Coding and optimal streaming Theorem 2 demonstrates that Coding is near optimal in terms of sustainable streaming rate during a flash crowd.

Flash Crowd Scenarios (Cont.)

Theorem 3 shows that Coding manages to guarantee very short initial buffering delays during a flash crowd. Theorem 2 and Theorem 3 suggest that the performance gap between Coding and optimal streaming scheme is small.

Flash Crowd Scenarios (Cont.)

Validation of the required relative server capacity in several different flash crowd scenarios

Impact of restricted neighborhoods on the playback quality

Highly Dynamic Scenarios The arrivals of new peers in current time No effect on the playback quality of the most urgent segment The departures of existing peers Central role in the playback quality

Highly Dynamic Scenarios (Cont.) Assumptions:

Highly Dynamic Scenarios (Cont.)

Theoretical and simulation results for relative additional server capacity to handle peer dynamics in the worst case The theoretical bound is tight when bandwidth supply barely exceeds bandwidth demand, while the bound is loose when supply outstrips demand.

Simulation results for relative additional server capacity to handle peer dynamics in the average case Only a small amount of additional server capacity is required, even when 50% peers leave the system.

Formal Proof of Sufficient Conditions-Theorem 1.

Formal Proof of Sufficient Conditions-Theorem 1. (Cont.)

Fraction of Redundant Blocks Linearly dependent coded blocks from upstream peers Waste of bandwidth resources Estimation of the fraction of redundant blocks Bandwidth utilization of Coding

Fraction of Redundant Blocks (Cont.) For the proof of Lemma 1. refer to “S. Deb, et al., Algebraic gossip: a network coding approach to optimal multiple rumor, IEEE Trans. on information theory

Fraction of Redundant Blocks (Cont.) With high probability any coded block from an upstream peer is useful to its downstream peer The space spanned by the coded blocks on the upstream peer is not a subspace of the space spanned on downstream peer.

Fraction of Redundant Blocks (Cont.)

The randomized encoding algorithm on an upstream peer does not take into account the coded blocks accumulated on its downstream peers Producing some redundant coded blocks Size of the Galois field q Upstream peer has no innovative coded blocks for its downstream peers The probability of such event is small The random push operations naturally create sufficient diversity

Simulation results for fraction of redundant coded blocks The fraction of redundancy induced by network coding is in the order of 0.001, even when the field size q is as small as 64

Comparison with Pull A comparison of playback quality between Coding and Pull under different peer dynamic scenarios

The change of playback quality over time in Coding and Pull under a typical flash crowd scenario and a highly dynamic scenario

Summary Analytically investigation of the performance of streaming systems with network coding Simple and effective streaming Extensive large scale simulations The analytical results have been validated Demonstrating the advantages of network coding based protocols over traditional pull based streaming protocols.