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Adaptive Multi-source Streaming in Heterogeneous P2P Networks

This paper presents a novel approach for adaptive multi-source streaming in peer-to-peer (P2P) networks, addressing the challenges of bandwidth variability and sender heterogeneity. The proposed Peer-to-Peer Adaptive Layered Streaming (PALS) mechanism leverages a receiver-driven architecture, dynamically adjusting the target quality based on real-time bandwidth measurements. The system employs a sliding window technique to optimize packet requests from various sources, enhancing the quality while minimizing packet loss. Performance evaluations demonstrate that PALS effectively manages bandwidth fluctuations and improves streaming quality.

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Adaptive Multi-source Streaming in Heterogeneous P2P Networks

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  1. Adaptive Multi-source Streaming in Heterogeneous Peer-to-peer Network Vikash Agarwa; Reza Rejaie Twelfth Annual Multimedia Computing and Networking (MMCN ’05)

  2. Agenda • Introduction • Assumptions and Target Environment • PAL • Sliding Window • Quality Adaptation • Packet Assignment • Performance Evaluation • Conclusion

  3. Introduction • P2P streaming requires • Overlay construction • Content Delivery • Challenges • The available bandwidth from each sender is not known a priori and could significantly change during a session • Different senders have different bandwidth and RTT, or even leave

  4. Introduction • Multi-source MDC : Commonly used separate layer for each sender Drawback: • Available bandwidth may be too small • Available bandwidth may be too large • Unpredictable variation needs complicates inter-layer synchronization

  5. Introduction • Propose P2P Adaptive Layered Streaming (PALS) • Receiver driven • Heterogeneous senders • Periodically determine the target quality (i.e. the number of layers) that can be streamed • Relatively low amount of receiver buffering • Can be used in non-interactive streaming application

  6. Assumptions & Target Environment • Request a single video from group of senders • UDP connections and TCP-friendly congestion control • Sender differs from bandwidth and RTT and bottlenecks • Assume provided information of senders (PRO) • Assume layered coded • Assume all layers have same constant bit rate, C.

  7. PAL Mechanism • Receiver monitors EWMA bandwidth from each senders • Sliding Window (SW) • Tewma = Tjewma • Predict total number of incoming packet : K = (Tewma*∆)/pktsze • Quality Adaptation (QA) • Number of active layer that can be played • Packet from each layer to be request in future

  8. PAL Mechanism • Packet Assignment (PA) • Divides selected packets into disjoint subnet • Sends a separate request to each sender • Each sender maintains a single list of pending packets for each receiver peer

  9. Sliding Window Δ : buffering window = 5*SRTT δ : minimum threshold. If exceeds -> slide window

  10. Sliding Window • Drawback : • Duplicate request -> low • Overestimating bandwidth -> overwriting request, packet ordering • Underestimating bandwidth -> idle, further request (window overlap)

  11. Quality Adaptation • Invoke once per window • If (Tewma >= n * C) , utilize the excess bandwidth to request future packet • BUFadd : Total buffer data before adding a layer • If (Tewma < n * C), drain the buffer~~, drop top layer

  12. Packet Assignment • Weighted round-robin • Partially Available content : two pass • Treat all senders have the entire content • Per-packet assignment

  13. Performance Evaluation Window overlap = 20% Nc = 5 C = 80KBytes/sec δ=2*SRTTmax

  14. Quality comparing single and multiple layer static streaming

  15. Duplicate packet • Low duplicate packets – 8% • Duplicates decrease as window size increase • Loss remains below 5%

  16. Dynamic of bandwidth variation

  17. Conclusion • Multiple-source MDC streaming • Sliding Window uses for predicting number of incoming packets • Quality Adaptation uses for balance between the quality and the potential loss • Packet Assignment deals with the packet distribution among the senders

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