1. Multimedia Computing and Networking Jan 2002 1 Reduced Energy Decoding of MPEG Streams Malena Mesarina, HP Labs/UCLA CS Dept Yoshio Turner, HP Labs

2. 2 System Environment Portable client – limited battery life Multimedia server – ample compute/storage Application – stored media streaming with MPEG decoding performed by the client

3. 3 Problem Tradeoff – client energy consumption increases with media stream quality User should be able to choose the operating point to balance quality and battery life Goal: improve the energy/QoS tradeoff by reducing the energy consumption required for each level of media quality

4. 4 Approach Idea: exploit the ample resources of servers to improve client battery life Client supports a discrete set of voltages and clock frequencies –  voltage   speed,  energy consumption –Dynamic Voltage Scaling – DVS Server pre-processes (offline) stored media –Computes frame decoding order –Assigns voltage/frequency per frame –Transmits schedule to client for DVS execution

5. 5 Contributions New DVS scheduling algorithm –Minimizes CPU energy consumption –Satisfies timing constraints –Satisfies buffering constraints Quantification of the energy-QoS tradeoff Evaluation of the impact of DVS and client design parameters (processor speed, buffering) on the energy-QoS tradeoff

6. 6 Decoding Hardware Organization decoder I0 P1 B3 Past Future Reference Buffers Video Display Buffer Decoding order: I0 P1 B2 B3 P2... Input fifo Audio Display Buffer

7. 7 Naïve Scheduling is Bad a0 [Time/Energy] a1 [Time/Energy] V_hi 2 / 127 / 6 V_lo 8 / 411 / 3 Deadline 1424 Start time 00 v0 [Time/Energy] v1 [Time/Energy] V_hi2 / 103 / 5 V_lo7 / 57 / 4 Deadline1020 Start time 010 AudioVideo Voltage Naive scheduling = EDF task order + greedy voltage assignment.

8. 8 DVS Scheduling Algorithm Goal : minimize energy consumption –For a uni-processor client find voltage-frequency settings per frame and interleaved order of decoding frames Subject to the following constraints –Frames within a stream are in a fixed decoding order –Frame decode interdependence (I-, P-, B-frames) –Display rates for video (33 fps) and audio (44 Khz) –Audio/Video synchronization: 80 ms –Limited client display buffer capacity

9. 9 DVS Scheduling Algorithm (continued) Approach: dynamic programming –Find the energy optimal subschedule that completes the first i video and j audio tasks by time t, over search space (i,j,t). Report the best results over all possible t for the full media. –Search space is reduced by exploiting our knowledge of the constraints

10. 10 Main Challenges Frame decoding inter-dependencies: B-frames depend on future P-frames –Decoding order not equal to display order –Construct a mapping function from frame decode number to frame display number in order to compute correct deadlines Limited buffer capacity –Algorithm must have overflow avoidance mechanism Multiple voltage levels and possible frame decoding orders –Intractable search space, pruning necessary

11. 11 Fixed Display Buffer Capacity Overflow prevention: Translate buffer constraints to timing constraints Assign minimum decoding start times to tasks Suppose display buffer is full (contains previously decoded frames) Earliest time to enqueue (min start time) for B5 is when head frame I0 leaves buffer to be displayed The head frame I0 is identified using the frame display order and buffer capacity

12. 12 Key to Tractable Execution Limit the number of combinations of (i, j, t) –Limit the range of subschedule completion times t (time windows) –Limit the combinations of (i,j) by detecting “dead-end” subschedules  small number of (i,j) pairs, each with small time window

13. 13 Limiting Completion Times: Time Window A window represents possible completion times of i video and j audio tasks. Lower Bound, Tmin(i,j): earliest time when the last task in both streams can complete Upper Bound, Tmax(i,j): latest time when the last task in both streams can complete Tmax – Tmin ~ (1/frame_rate) * buffersize

14. 14 Time Window Example tmin[i+1,j]tmax[i+1,j] (i + 1)-th video frame tmin[i,j] tmax[i,j] i-th video frame Time

15. 15 Only some (i,j) subschedules lead to complete schedule Scheduling (i,j) = (10,14) POSSIBLE BUT Scheduling (i,j) = (10,15) is NOT POSSIBLE because AUDIO BUFFER OVERFLOWS!  (i,j) is limited by the buffer size Algorithm Complexity: O(N *B)  O(B* T S ) = O(N * B 2 * Ts) 11 12 13 14 10 Video frame in display VideoAudio 10 Audio frame in display N = #frames B = buffer size T s = 1/frame rate

16. 16 Performance Evaluation: Energy vs QoS Exploration Variability in frame execution times –Potential for energy reduction? Energy savings vs picture quality –For what range of quality is DVS helpful ? –How much improvement is in that range ? Impact of client design parameters on energy vs QoS –How does processor speed change tradeoff ? –Will extra buffering ease schedulability? Reduce energy?

17. 17 Frame Execution Times

18. 18 Energy-QoS tradeoff: Fast Processor + Fixed Buffer Size Pentium 3 (1.9V@500MHz, 1.4V@316 MHz) Display buffers: 2 for video, 2 for audio Scale factor = frame pixels/max frame pixels scale factor 0 10 20 30 40 50 0.70.750.80.850.90.951 19%   47% Energy improvement over range of high resolution scale factors, 0.6 to 1

19. 19 Energy-QoS tradeoff: Slow Processor + Variable Buffer Size Pentium 2: (1.7V@300MHz, 1.4V@225MHz) Variable buffering : (video,audio)  (1,1), (3,3) (6,3) Increasing buffering does not improve energy significantly Extra buffers enable decoding of higher QoS video

20. 20 Summary and Conclusions Offline algorithm finds a low energy schedule that respects: –Timing constraints (display rate, synchronization) –Limited memory at client DVS significantly reduces energy consumption Increasing buffer size –No impact on energy but –Enables higher video quality

21. 21 Future Work Online scheduling –Offline schedule represents lower bound on energy Exploration of other tradeoff media parameters (frame rate, display brightness) Implementation with progressive coding schemes (JPEG2000)

22. 22 Experimental Setup Fixed voltage/frequency processors: P3 and P2 Computed time/energy per frame at fixed voltage Extrapolated time/energy per frame at other operational core voltages Assumptions: –Frequency is inverse proportional to gate delay –Cycles/frame remains constant for different frequencies –Power dissipation constant for a given voltage setting

23. 23 Extrapolation Example Given: V hi, F hi,, T hi, P hi –F lo = F hi *  hi /  lo = F hi * V hi /(V hi -V t ) 2 (1) –T lo = cycles/F lo = F hi * T hi /F lo (2) –P lo = P hi * (F lo * V lo 2) /(F hi * V hi 2 ) –E lo = P lo * T lo (3)

24. 24 Related Work Problem we address –Real-time scheduling of non-preemptable tasks with precedence constraints Other real-time schedulers treat different cases –[1] Liu and Layland, “Scheduling algorithms for multiprogramming in a hard-real-time environment” –[2] Yao et al. “A scheduling model for reduced CPU energy” No precedence constraints and preemptable tasks –[3] Hong et al. “Power optimization of variable voltage core-based systems” Heuristics for non-preemptable tasks but no precedence constraints

25. 25 Frame Interdependence Map frame number i in decoding order to frame number d(i) in display order