Neural Adaptive Video Streaming with Pensieve

https://dl.acm.org/doi/abs/10.1145/3098822.3098843

• Motivation

  • Users start leaving if video doesn't play in 2 seconds

  • Dynamic streaming over HTTP (DASH)

    • 

    • 

      • Bitrate: higher --> higher quality

      • Video rate > capacity --> empty playback buffer

  • Adaptive Bitrate (ABR) Algorithms

    • Variable bit rate adaptively: depends on the network and playback buffer condition

    • Large impact on QoE

  • Why is ABR challenging?

    • Network throughput is variable & uncertain

    • Conflicting QoE goals

      • Bitrate

      • Rebuffering time

      • Smoothness

    • Cascading effects of decisions

• Contribution: Pensieve

  • Learns ABR algorithm automatically through experience

  • Contributions

    • First network control system use modern "deep" reinforcement learning

    • Delivers 12-25% better QoE, with 10-30% less rebuffering than previous ABR algorithms

    • Tailors ABR decisions for different network conditions in a data-driven way

• Previous Fixed ABR algoithms

  • Rate-based: pick bitrate based on predicted throughput

    • FESTIVE, PANDA, CS2P

  • Buffer-based: pick bitrate based on buffer occupancy

    • BBA, BOLA

  • Hybrid: use both throughput prediction & buffer occupancy

    • PBA, MPC

      • MPC: maximize QoE(t, t+T) subject to system dynamics

        • Problem: needs accurate throughput model

        • Use simplified prediction (conservative throughput prediction)

  • Simplified inaccurate model leads to suboptimal performance

• Solution: learn from video streaming sessions in actual network conditions

Reinforcement Learning

• Learning agent interacts with environment

• 
• Goal: maximize the cumulative reward

• Application

  • Finite horizon control problem

  • 
  • Collect experience data: trajectory of [state, action, reward]

  • Train: estimate from empirical data

• Good at

  • Learn the dynamics directly from experience

  • Optimize the high level QoE objective end-to-end

  • Extract control rules from raw high-dimensional signals

• Training system

  • Large corpus of network traces

  • Video playback

  • Model update

• Trace-driven Evaluation

  • Data set, video, video player, video server

  • 
  • Improves the best previous scheme by 12-25% and is within 9-14% of the offline optimal

• QoE breakdown

  • Achieve the best collective QoE

  • Reduce rebuffering by 10-32% over second best algorithm

• Can this learning generalize?

  • Training and testing data are drown from the same dataset now

  • Video streaming might be in different kinds of network

  • Generate synthetic trace from a Hidden Markov model

    • Covering the network conditions?

    • Covers a wide range of average throughput and network variation

  • 5% degradation compared with Pensieve trained on real network trace

• Lessons we learned

  • Build a fast experimentation / simulation platform

    • Doesn't model TCP, and the underlying network packet

    • Coarse-grain chunk simulator

  • Data diversity is more important than "accuracy"

    • Want the agent to experience a diverse mix of network variation

  • Think carefully about controller state space (observation signals)

    • Too large a state space --> slow and difficult learning

    • Too small a state space --> loss of information

    • When in doubt, include rather than cut the signal